SCRIPT Bulk High-Level Analysis
Goals
- Identify transcriptional patterns in AM associated with IAV, COVID-19, and common to both
- Determine source of IL6 and IL1B in the lung (if possible)
Setup
Load packages
library(ggplot2)
library(ggplotify)
library(ggsignif)
library(Cairo)
library(DESeq2)
library(pheatmap)
library(RColorBrewer)
library(viridis)
library(patchwork)
library(factoextra)
library(parallel)
library(uwot)
library(WGCNA)
library(ggsci)
library(topGO)
library(GO.db)
library(org.Hs.eg.db)
library(parallel)
library(doParallel)
library(biomaRt)
library(openxlsx)
registerDoParallel(makeCluster(12))
register(MulticoreParam(12))
options(gsubfn.engine = "R")
set.seed(12345)
source("~/utils/R/pretty_MA_plot.R")
source("~/utils/R/k_means_figure.R")
source("~/utils/R/plotPCA_manual.R")
fig2_pal = pal_npg("nrc")(9)
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux Server 7.5 (Maipo)
Matrix products: default
BLAS: /hpc/software/lapack/3.6.0_gcc/lib64/libblas.so.3.6.0
LAPACK: /hpc/software/lapack/3.6.0_gcc/lib64/liblapack.so.3.6.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] openxlsx_4.1.5 ggsignif_0.6.0 GenomicAlignments_1.22.1 Rsamtools_2.2.3
[5] Biostrings_2.54.0 XVector_0.26.0 pryr_0.1.4 rasterize_0.1
[9] sp_1.4-2 ggrastr_0.1.9 Cairo_1.5-12 ggplotify_0.0.5
[13] RPushbullet_0.3.3 ggrepel_0.8.2 gtools_3.8.2 biomaRt_2.42.1
[17] corrplot_0.84 ggforce_0.3.2 googledrive_1.0.1 googlesheets4_0.2.0
[21] spgs_1.0-3 readxl_1.3.1 rtracklayer_1.46.0 forcats_0.5.0
[25] stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4 readr_1.3.1
[29] tidyr_1.1.0 tibble_3.0.1 tidyverse_1.3.0 doParallel_1.0.15
[33] iterators_1.0.12 foreach_1.5.0 org.Hs.eg.db_3.10.0 topGO_2.38.1
[37] SparseM_1.78 GO.db_3.10.0 AnnotationDbi_1.48.0 graph_1.64.0
[41] ggsci_2.9 WGCNA_1.69 fastcluster_1.1.25 dynamicTreeCut_1.63-1
[45] uwot_0.1.8 Matrix_1.2-18 factoextra_1.0.7 patchwork_1.0.1
[49] viridis_0.5.1 viridisLite_0.3.0 RColorBrewer_1.1-2 pheatmap_1.0.12
[53] DESeq2_1.26.0 SummarizedExperiment_1.16.1 DelayedArray_0.12.3 BiocParallel_1.20.1
[57] matrixStats_0.56.0 Biobase_2.48.0 GenomicRanges_1.38.0 GenomeInfoDb_1.22.1
[61] IRanges_2.20.2 S4Vectors_0.24.4 BiocGenerics_0.34.0 ggplot2_3.3.2
loaded via a namespace (and not attached):
[1] backports_1.1.8 Hmisc_4.4-0 BiocFileCache_1.10.2 splines_3.6.3 digest_0.6.25
[6] htmltools_0.5.0 fansi_0.4.1 magrittr_1.5 checkmate_2.0.0 memoise_1.1.0
[11] cluster_2.1.0 annotate_1.64.0 modelr_0.1.8 askpass_1.1 prettyunits_1.1.1
[16] jpeg_0.1-8.1 colorspace_1.4-1 rappdirs_0.3.1 blob_1.2.1 rvest_0.3.5
[21] haven_2.3.1 xfun_0.15 crayon_1.3.4 RCurl_1.98-1.2 jsonlite_1.7.0
[26] genefilter_1.68.0 impute_1.60.0 survival_3.1-8 glue_1.4.1 polyclip_1.10-0
[31] gtable_0.3.0 zlibbioc_1.32.0 scales_1.1.1 DBI_1.1.0 Rcpp_1.0.4.6
[36] progress_1.2.2 xtable_1.8-4 htmlTable_2.0.0 gridGraphics_0.5-0 foreign_0.8-75
[41] bit_1.1-15.2 preprocessCore_1.48.0 Formula_1.2-3 htmlwidgets_1.5.1 httr_1.4.1
[46] acepack_1.4.1 ellipsis_0.3.1 farver_2.0.3 pkgconfig_2.0.3 XML_3.99-0.3
[51] nnet_7.3-12 dbplyr_1.4.4 locfit_1.5-9.4 tidyselect_1.1.0 rlang_0.4.6
[56] munsell_0.5.0 cellranger_1.1.0 tools_3.6.3 cli_2.0.2 generics_0.0.2
[61] RSQLite_2.2.0 broom_0.5.6 knitr_1.29 bit64_0.9-7 fs_1.4.2
[66] zip_2.0.4 nlme_3.1-144 xml2_1.3.2 compiler_3.6.3 rstudioapi_0.11
[71] beeswarm_0.2.3 curl_4.3 png_0.1-7 reprex_0.3.0 tweenr_1.0.1
[76] geneplotter_1.64.0 stringi_1.4.6 lattice_0.20-38 vctrs_0.3.1 pillar_1.4.4
[81] lifecycle_0.2.0 BiocManager_1.30.10 data.table_1.12.8 bitops_1.0-6 R6_2.4.1
[86] latticeExtra_0.6-29 gridExtra_2.3 vipor_0.4.5 codetools_0.2-16 MASS_7.3-51.5
[91] assertthat_0.2.1 openssl_1.4.2 withr_2.2.0 GenomeInfoDbData_1.2.2 hms_0.5.3
[96] rpart_4.1-15 rvcheck_0.1.8 lubridate_1.7.9 base64enc_0.1-3 ggbeeswarm_0.6.0 Import data
mp = readRDS("/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_script_bulk_des_no_hURNA.rds")
mp$covid_confirmed = factor(mp$covid_confirmed)
mp$sex = factor(mp$gender)
#set up for comparison by diagnosis
mp$Diagnosis = gsub("-| ", "_", as.character(mp$pna_type))
mp$Diagnosis = factor(mp$Diagnosis,
levels = c("Healthy_Control", "Non_Pneumonia_Control", "COVID_19",
"Other_Viral_Pneumonia", "Other_Pneumonia"))
mp$neutrophilic = mp$percent_neutrophils > 50
#for modeling, etc
mp$finite_day_of_intubation = mp$day_of_intubation
mp$finite_day_of_intubation[is.infinite(mp$finite_day_of_intubation)] = NA
mp$day0_sample = factor(mp$day_of_intubation >= 0 & mp$day_of_intubation <=2)
mp$day0_sample[is.na(mp$day0_sample)] = FALSE
design(mp) = as.formula("~ Diagnosis")
metadata = as.data.frame(colData(mp))Summary stats
Number of samples overall
table(mp$pna_type, useNA = "always")
Healthy Control Non-Pneumonia Control COVID-19 Other Viral Pneumonia Other Pneumonia
5 24 82 29 103
<NA>
0 Number of patients
bulk_patients = colData(mp) %>%
as.data.frame() %>%
dplyr::select(study_id, covid_confirmed, pna_type) %>%
unique()
table(bulk_patients$covid_confirmed)
FALSE TRUE
135 51 table(bulk_patients$pna_type)
Healthy Control Non-Pneumonia Control COVID-19 Other Viral Pneumonia Other Pneumonia
5 23 51 23 84 Number of serial samples
n_samples = table(metadata$patient_id)
serial_patients = names(n_samples[n_samples > 1])
length(serial_patients)[1] 41Distribution of days
ggplot(metadata, aes(x = day_of_intubation)) +
geom_histogram(fill = "dodgerblue4")
PCA
Calculate and organize
mp_vst = vst(mp, nsub = 3000, fitType = "local") #know from later that local is best
mp_counts = counts(estimateSizeFactors(mp), normalized = T)
pca_data = plotPCA_manual(mp_vst,
intgroup = c("covid_confirmed"),
pcs = 10)
pca_data$data = left_join(pca_data$data,
as.data.frame(colData(mp)),
by = c("name" = "sample", "covid_confirmed"))
#merge counts and pca data
gene_conv = as.data.frame(rowData(mp)) %>%
rownames_to_column(var = "ensembl_gene_id")
merge_counts = mp_counts %>%
t() %>%
as.data.frame() %>%
rownames_to_column(var = "name")
#merge together for featureplot dataset
fp_data = left_join(pca_data$data, merge_counts, by = "name")How many PCs?
#scree plot
fviz_eig(pca_data$pca,
barfill = "dodgerblue4",
xlab = "Principal Component",
ylab = "Percent Variance Explained",
main = "",
ncp = 50,
ggtheme = theme_bw(),
addlabels = T) #4-5 PCs will do it
Examine loadings
loadings = pca_data$pca$rotation %>%
as.data.frame() %>%
rownames_to_column(var = "ensembl_gene_id") %>%
right_join(gene_conv, .)Joining, by = "ensembl_gene_id"PC1: MoAM Character
ggplot(fp_data, aes(x = PC1, y = PC2, shape = covid_confirmed, color = percent_total_CD206_high)) +
geom_point(size = 3)
fabp4_plot = ggplot(fp_data, aes(x = PC1, y = PC2, color = log2(ENSG00000170323))) +
geom_point() +
scale_color_viridis(name = "Log2(FABP4 Counts)")
spp1_plot = ggplot(fp_data, aes(x = PC1, y = PC2, color = log2(ENSG00000118785))) +
geom_point() +
scale_color_viridis(name = "Log2(SPP1 Counts)")
fabp4_plot + spp1_plot
PC2: Patterns of activation (also by milieu)
ggplot(fp_data, aes(x = PC1, y = PC2, shape = covid_confirmed, color = C_Reactive_Protein)) +
geom_point(size = 3)
ggplot(fp_data, aes(x = PC1, y = PC2, shape = covid_confirmed, color = LDH)) +
geom_point(size = 3)
ggplot(fp_data, aes(x = PC1, y = PC2, shape = covid_confirmed, color = FERRITIN)) +
geom_point(size = 3)
inhba_plot = ggplot(fp_data, aes(x = PC1, y = PC2, color = log2(ENSG00000122641))) +
geom_point() +
scale_color_viridis(name = "Log2(INHBA)")
il1b_plot = ggplot(fp_data, aes(x = PC1, y = PC2, color = log2(ENSG00000125538))) +
geom_point() +
scale_color_viridis(name = "Log2(IL1b)")After adding healthy controls this is longer true.
PC3: Male/female?
ggplot(fp_data, aes(x = PC2, y = PC3, color = gender)) +
geom_point(size = 3)
xist_plot = ggplot(fp_data, aes(x = PC2, y = PC3, color = log2(ENSG00000229807))) +
geom_point() +
scale_color_viridis(name = "Log2(XIST)")Not really
Condition
ggplot(fp_data, aes(x = PC1, y = PC2, color = Diagnosis)) +
geom_point(size = 2) +
stat_ellipse()
Day of intubation
ggplot(fp_data, aes(x = PC1, y = PC2, color = log2(day_of_intubation))) +
geom_point(size = 3)
No clear separation.
Determine best dispersion estimates
Parametric
#note: DESeq is refitting far too many genes. Need to turn this off.
mp_des_parametric = DESeq(mp,
fitType = "parametric",
parallel = T,
minReplicatesForReplace = Inf)estimating size factors
estimating dispersions
gene-wise dispersion estimates: 12 workers
mean-dispersion relationship
final dispersion estimates, fitting model and testing: 12 workersplotDispEsts(mp_des_parametric, cex = 0.1)
This fit really isn’t bad, but overestimates at lower expression (probably not a huge deal).
Local
mp_des_local = DESeq(mp,
fitType = "local",
parallel = T,
minReplicatesForReplace=Inf)estimating size factors
estimating dispersions
gene-wise dispersion estimates: 12 workers
mean-dispersion relationship
final dispersion estimates, fitting model and testing: 12 workersplotDispEsts(mp_des_local, cex = 0.1)
mp_des = mp_des_local
rm(mp_des_local, mp_des_parametric)Still not perfect, but a lot better.
#used a few times later; safest to put here
#identify infected samples
non_human_genes = subset(gene_conv, !(grepl("^ENSG", ensembl_gene_id))) #human genes all have this prefix
non_human_genes$gene_name[non_human_genes$ensembl_gene_id == "gene-UJ99_s6gp1"] = "NA" #neuraminidase was misread
non_human_genes$virus = factor(ifelse(grepl("UJ99", non_human_genes$ensembl_gene_id),
yes = "Influenza A/California/07/2009",
no = "SARS-CoV-2"))
cov2_genes = subset(non_human_genes, grepl("SARS|GU280", ensembl_gene_id))
iav_genes = subset(non_human_genes, grepl("UJ99", ensembl_gene_id))
flu_counts = counts(mp_des, normalized = T)[iav_genes$ensembl_gene_id, ]
flu_detected = colnames(flu_counts[, colSums(flu_counts) > 0])
cov2_counts = counts(mp_des, normalized = T)[cov2_genes$ensembl_gene_id, ]
cov2_detected = colnames(cov2_counts[, colSums(cov2_counts) > 0])
mp_des$sars_cov2_detected = factor(mp_des$sample %in% cov2_detected)
mp_des$iav_h1n1_detected = factor(mp_des$sample %in% flu_detected)
## Replicating CoV2 samples
### overall
antisense_counts = cov2_counts["SARS_CoV_2_antisense_genome", ]
sense_counts = cov2_counts[2:nrow(cov2_counts), ]
replicating_cov2 = intersect(names(antisense_counts)[antisense_counts > 0],
colnames(sense_counts[, colSums(sense_counts) > 0]))
### just covid
percent_detected_covid = length(unique(subset(metadata, sample %in% cov2_detected & covid_confirmed == TRUE)$study_id)) / length(unique(subset(metadata, covid_confirmed == TRUE)$study_id)) * 100
percent_detected_covid[1] 66.66667percent_replicating_covid = length(unique(subset(metadata, sample %in% replicating_cov2 & covid_confirmed == TRUE)$study_id)) / length(unique(subset(metadata, covid_confirmed == TRUE)$study_id)) * 100
percent_replicating_covid[1] 25.4902percent_replicating_covid / percent_detected_covid * 100[1] 38.23529Sanity checks
SARS-CoV-2
Does expression of viral genes correlate with covid diagnosis?
cov2_counts = lapply(cov2_genes$ensembl_gene_id, function(x){
counts = plotCounts(mp,
gene = x,
intgroup = "Diagnosis",
returnData = T,
pc = T)
counts$gene = x
return(counts)})
cov2_counts = bind_rows(cov2_counts)
ggplot(cov2_counts, aes(x = Diagnosis, y = count, fill = gene)) +
geom_boxplot() +
scale_y_log10()
Now that we have all of the necessary metadata, this looks fantastic.
Do counts cluster by virus and diagnosis?
viral_counts = counts(mp_des, normalized = T)
viral_counts = viral_counts[non_human_genes$ensembl_gene_id, ]
viral_counts = log10(viral_counts + 0.5)
virus_detection = vector(mode = "character", length = ncol(mp_des))
for(i in 1:ncol(mp_des))
{
has_covid = mp_des$covid_confirmed[i] == TRUE
has_iav_h1n1 = mp_des$INFLUENZA_A_H1_2009[i] == "Positive"
has_other_coronavirus = mp_des$any_coronavirus_non_covid[i] == TRUE
#recode NA as FALSE for safety
if(is.na(has_iav_h1n1))
{
has_iav_h1n1 = FALSE
}
if(is.na(has_other_coronavirus))
{
has_other_coronavirus = FALSE
}
if(has_covid == TRUE && has_iav_h1n1 == TRUE && has_other_coronavirus == TRUE)
{
virus_detection[i] = "SARS-CoV-2 & Other Coronavirus & Influenza A/California/07/2009"
} else if(has_covid == TRUE && has_iav_h1n1 == FALSE && has_other_coronavirus == FALSE)
{
virus_detection[i] = "SARS-CoV-2"
} else if(has_covid == FALSE && has_iav_h1n1 == TRUE && has_other_coronavirus == FALSE)
{
virus_detection[i] = "Influenza A/California/07/2009"
} else if(has_covid == FALSE && has_iav_h1n1 == FALSE && has_other_coronavirus == TRUE)
{
virus_detection[i] = "Other Coronavirus"
} else if(has_covid == TRUE && has_iav_h1n1 == TRUE && has_other_coronavirus == FALSE)
{
virus_detection[i] = "SARS-CoV-2 & Influenza A/California/07/2009"
} else if(has_covid == TRUE && has_iav_h1n1 == FALSE && has_other_coronavirus == TRUE)
{
virus_detection[i] = "SARS-CoV-2 & Other Coronavirus"
} else if(has_covid == FALSE && has_iav_h1n1 == TRUE && has_other_coronavirus == TRUE)
{
virus_detection[i] = "Other Coronavirus & Influenza A/California/07/2009"
} else
{
virus_detection[i] = NA
}
}
mp_des$detected_viruses = factor(virus_detection)
diagnosis_annotation = as.data.frame(colData(mp_des)) %>%
dplyr::select(Diagnosis = detected_viruses)
gene_annotation = non_human_genes %>%
remove_rownames() %>%
column_to_rownames("ensembl_gene_id") %>%
dplyr::select(`Viral Origin` = virus)
gene_names = non_human_genes$gene_name
gene_names[gene_names == "SARS_CoV_2_antisense_genome"] = "Antisense"
viral_expression_heatmap = pheatmap(viral_counts,
clustering_method = "ward.D2",
show_colnames = F,
color = inferno(100),
annotation_col = diagnosis_annotation,
annotation_row = gene_annotation,
labels_row = gene_names,
annotation_colors = list(Diagnosis = c("SARS-CoV-2" = fig2_pal[1], "Other Coronavirus" = fig2_pal[2],
"Influenza A/California/07/2009" = fig2_pal[3], "Other Coronavirus & Influenza A/California/07/2009" = fig2_pal[4]),
`Viral Origin` = c("SARS-CoV-2" = fig2_pal[1], "Influenza A/California/07/2009" = fig2_pal[3])),
angle_col = 45,
annotation_names_row = F,
fontsize = 24)
Patient 1174
This patient apparently met criteria for COVID well before the first known cases in Chicago
cov2_counts = counts(mp_des, normalized = T)[cov2_genes$ensembl_gene_id, ]
samples_1174 = mp_des$study_id == "1174"
samples_1174[is.na(samples_1174)] = FALSE
tubes_1174 = mp_des$sample[samples_1174]
cov2_counts_1174 = cov2_counts[, tubes_1174]
cov2_counts_1174SARS_CoV_2_antisense_genome gene-GU280_gp01 gene-GU280_gp02 gene-GU280_gp03
0 0 0 0
gene-GU280_gp04 gene-GU280_gp05 gene-GU280_gp06 gene-GU280_gp07
0 0 0 0
gene-GU280_gp08 gene-GU280_gp09 gene-GU280_gp10 gene-GU280_gp11
0 0 0 0 No detection, at least in macrophages.
Do CoV2 reads decrease over time?
Checking for viral clearance
covid_cases = mp_des[, mp_des$covid_confirmed == TRUE]
covid_counts = counts(covid_cases, normalized = T)
covid_counts = covid_counts[cov2_genes$ensembl_gene_id, ]
covid_counts = covid_counts %>%
as.data.frame() %>%
rownames_to_column(var = "gene") %>%
pivot_longer(cols = "340195046":"340239867",
names_to = "sample",
values_to = "Counts") %>%
left_join(., non_human_genes, by = c("gene" = "ensembl_gene_id")) %>%
dplyr::select(-c(gene, virus)) %>%
left_join(., as.data.frame(colData(mp_des)), by = "sample")
ggplot(covid_counts, aes(x = day_of_intubation, y = Counts)) +
facet_wrap(~ gene_name, scales = "free_y") +
geom_point() +
geom_smooth(se = F)
Unfortunately this is somewhat binarized. Better to treat it as such.
time_cor
Spearman's rank correlation rho
data: covid_counts_binarized$all_viral_counts and covid_counts_binarized$day_of_intubation
S = 35853, p-value = 0.0008307
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.445437 Export for modeling core
col_types = unlist(lapply(metadata, class))
list_cols = names(col_types[col_types == "AsIs"])
safe_metadata = metadata %>%
dplyr::select(-c(all_of(list_cols))) %>%
as.data.frame()
write.csv(safe_metadata, "/projects/b1038/Pulmonary/Workspace/COVID19_bal_ms/200709_bulk_metadata.csv")
write.csv(counts(mp_des, normalized = F), "/projects/b1038/Pulmonary/Workspace/COVID19_bal_ms/200709_bulk_counts_raw.csv")
write.csv(counts(mp_des, normalized = T), "/projects/b1038/Pulmonary/Workspace/COVID19_bal_ms/200709_bulk_counts_deseq2_normalized.csv")Which samples are worth sequencing further?
First batch
good_samples = mp_des[, ((!is.na(mp_des$RIN) & mp_des$RIN >= 7) &
mp_des$STAR_mqc_generalstats_star_uniquely_mapped_percent >= 30 &
mp_des$featureCounts_mqc_generalstats_featurecounts_percent_assigned >= 30) &
mp_des$RNA_concentration_pg_ul >= 125 |
(mp_des$iav_h1n1_detected == TRUE | mp_des$sars_cov2_detected == TRUE)]
selection_data = as.data.frame(colData(good_samples)) %>%
dplyr::select(sample, tc_pt_study_id, RIN, STAR_mqc_generalstats_star_uniquely_mapped_percent,
featureCounts_mqc_generalstats_featurecounts_percent_assigned, iav_h1n1_detected,
sars_cov2_detected, covid_confirmed, batch, RNA_concentration_pg_ul, pna_type)
selection_dataKeepers:
- 340224808 (IAV) - 304017553 (IAV + likely another coronavirus) - 340239805 (Cov2) - 340233896 (Cov2) - 340239790 (Cov2) - 340239789 (Cov2) - 304012699 (Other – likely another coronavirus) - 300312389 (other) - 304020362 (other)
keepers = selection_data %>%
dplyr::filter(sample %in% c(304020298, 340224808, 304017553, 340239805, 340233896,
340239790, 340239789, 304012699, 300312389, 304020362)) %>%
mutate(vol_for_250_pg = round(250 / RNA_concentration_pg_ul, digits = 3))
keepers$dilution = ifelse(keepers$vol_for_250_pg > 0.5,
yes = 1,
no = ifelse(keepers$vol_for_250_pg > 0.1,
yes = 5,
no = 20))
keepers$dilution_vol_for_250_pg = round(250 / (keepers$RNA_concentration_pg_ul / keepers$dilution), digits = 3)
write.csv(keepers, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200612_resequencing_samples.csv")
keepersSignatures associated with SARS-CoV-2
Single genes
IL6
il6_counts = plotCounts(mp_des,
gene = "ENSG00000136244",
normalized = T,
intgroup = "covid_confirmed",
returnData = T) %>%
rownames_to_column(var = "sample") %>%
left_join(.,
metadata,
by = c("sample", "covid_confirmed"))
cor.test(il6_counts$count, il6_counts$percent_total_CD206_high)
Pearson's product-moment correlation
data: il6_counts$count and il6_counts$percent_total_CD206_high
t = -1.967, df = 214, p-value = 0.05047
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.2620931112 0.0002344531
sample estimates:
cor
-0.1332627 il6_plot = ggplot(il6_counts, aes(x = percent_total_CD206_high, y = count, color = covid_confirmed)) +
geom_point(size = 3) +
scale_y_log10() +
ylab("IL6 Counts")Later confirmed NSD by group. Clearly correlates with MoAM character.
IL1b
il1b_counts = plotCounts(mp_des,
gene = "ENSG00000125538",
normalized = T,
intgroup = "covid_confirmed",
returnData = T) %>%
rownames_to_column(var = "sample") %>%
left_join(.,
metadata,
by = c("sample", "covid_confirmed"))
cor.test(il1b_counts$count, il1b_counts$percent_total_CD206_high)
Pearson's product-moment correlation
data: il1b_counts$count and il1b_counts$percent_total_CD206_high
t = -3.8972, df = 214, p-value = 0.0001302
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.3779331 -0.1283454
sample estimates:
cor
-0.2574278 il1b_plot = ggplot(il1b_counts, aes(x = percent_total_CD206_high, y = count, color = covid_confirmed)) +
geom_point(size = 3) +
scale_y_log10() +
ylab("IL1b Counts")
il6_plot / il1b_plot
Later confirmed significantly downregulated, actually.
Basic DEA
Gene hits
Versus healthy controls
cov2_vs_hc_results = as.data.frame(results(object = mp_des,
contrast = c("Diagnosis", "COVID_19", "Healthy_Control"),
alpha = 0.05,
parallel = T))
cov2_vs_hc_results_ids = cov2_vs_hc_results %>%
rownames_to_column("ensembl_gene_id") %>%
left_join(., gene_conv) %>%
dplyr::filter(padj < 0.05 | !grepl("ENSG", ensembl_gene_id)) %>%
arrange(log2FoldChange)Joining, by = "ensembl_gene_id"write.csv(cov2_vs_hc_results_ids, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200717_standard_DEA_covid_over_healthycontrols.csv")
cov2_vs_hc_results_idsUpregulation of CoV-2 genes is gorgeous
Versus non-pna (sick) controls
cov2_vs_npc_results = as.data.frame(results(object = mp_des,
contrast = c("Diagnosis", "COVID_19", "Non_Pneumonia_Control"),
alpha = 0.05,
parallel = T))
cov2_vs_npc_results_ids = cov2_vs_npc_results %>%
rownames_to_column("ensembl_gene_id") %>%
left_join(., gene_conv) %>%
dplyr::filter(padj < 0.05 | !grepl("ENSG", ensembl_gene_id)) %>%
arrange(log2FoldChange)Joining, by = "ensembl_gene_id"write.csv(cov2_vs_npc_results_ids, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200717_standard_DEA_covid_over_nonpneumoniacontrols.csv")
cov2_vs_npc_results_idsVersus other viral PNA
cov2_vs_ovp_results = as.data.frame(results(object = mp_des,
contrast = c("Diagnosis", "COVID_19", "Other_Viral_Pneumonia"),
alpha = 0.05,
parallel = T))
cov2_vs_ovp_results_ids = cov2_vs_ovp_results %>%
rownames_to_column("ensembl_gene_id") %>%
left_join(., gene_conv) %>%
dplyr::filter(padj < 0.05 | !grepl("ENSG", ensembl_gene_id)) %>%
arrange(log2FoldChange)Joining, by = "ensembl_gene_id"cov2_vs_ovp_results_ids
write.csv(cov2_vs_ovp_results_ids, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200717_standard_DEA_covid_over_otherviralpneumonia.csv")Versus other PNA
cov2_vs_other_pna_results = as.data.frame(results(object = mp_des,
contrast = c("Diagnosis", "COVID_19", "Other_Pneumonia"),
alpha = 0.05,
parallel = T))
cov2_vs_other_pna_results_ids = cov2_vs_other_pna_results %>%
rownames_to_column("ensembl_gene_id") %>%
left_join(., gene_conv) %>%
dplyr::filter(padj < 0.05 | !grepl("ENSG", ensembl_gene_id)) %>%
arrange(log2FoldChange)Joining, by = "ensembl_gene_id"write.csv(cov2_vs_other_pna_results_ids, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200717_standard_DEA_covid_over_otherpneumonia.csv")
cov2_vs_other_pna_results_idsMA Plot
pretty_MA_plot(cov2_vs_ovp_results, custom_annotation = gene_conv)Heatmap
How many clusters does it take?
elbow = k_elbow(dge = mp_des, design = "~Diagnosis",
cores = 12,
minReps = Inf,
random_seed = 12345,
max_k = 25,
qval_cutoff = 0.01)
elbow5 seems fair.
intubation_pal = colorRampPalette(brewer.pal(n = 9, name = "Purples")[3:9])(max(mp_des$finite_day_of_intubation, na.rm = T) + 1)
intubation_pal[1:3] = rep("#000000",3) #highlight "D0"
cov2_kmeans_noclustering = k_means_figure(dge = mp_des,
design = "~Diagnosis",
k = 5,
go_annotations = "org.Hs.eg.db",
ensembl_db = "hsapiens_gene_ensembl",
legend_factors = c("Diagnosis", "finite_day_of_intubation"),
breaks = seq(-2, 2, length.out=101),
cores = 12,
minReps = Inf,
return_genes = T,
display_go_terms = F,
return_go_terms = F,
cluster_columns = F,
show_rownames = F,
baseMeanCutoff = 20,
random_seed = 12345,
qval_cutoff = 0.05,
annotation_colors = list(Diagnosis = c("Healthy_Control" = fig2_pal[6],
"Non_Pneumonia_Control" = fig2_pal[2],
"COVID_19" = fig2_pal[1],
"Other_Viral_Pneumonia" = fig2_pal[3],
"Other_Pneumonia" = fig2_pal[4]),
finite_day_of_intubation = intubation_pal),
custom_order = c(3, 5, 1, 2, 4),
sortColumns = T,
column_sort_factors = c("Diagnosis", "finite_day_of_intubation"))
cov2_kmeans_clustered = k_means_figure(dge = mp_des,
design = "~Diagnosis",
k = 5,
go_annotations = "org.Hs.eg.db",
ensembl_db = "hsapiens_gene_ensembl",
legend_factors = c("Diagnosis", "finite_day_of_intubation"),
breaks = seq(-2, 2, length.out=101),
cores = 12,
minReps = Inf,
return_genes = T,
display_go_terms = F,
return_go_terms = T,
cluster_columns = T,
show_rownames = F,
baseMeanCutoff = 20,
random_seed = 12345,
qval_cutoff = 0.05,
annotation_colors = list(Diagnosis = c("Healthy_Control" = fig2_pal[6],
"Non_Pneumonia_Control" = fig2_pal[2],
"COVID_19" = fig2_pal[1],
"Other_Viral_Pneumonia" = fig2_pal[3],
"Other_Pneumonia" = fig2_pal[4]),
finite_day_of_intubation = intubation_pal),
custom_order = c(3, 5, 1, 2, 4))
for(i in 1:length(cov2_kmeans_clustered$GO))
{
cov2_kmeans_clustered$GO[[i]]$cluster = i
}
cov2_kmeans_clustered$go_df = bind_rows(cov2_kmeans_clustered$GO) %>%
arrange(cluster, padj)
cov2_kmeans_clustered$expression = counts(mp_des, normalized = T) %>%
as.data.frame() %>%
rownames_to_column("ensembl_gene_id") %>%
dplyr::filter(ensembl_gene_id %in% cov2_kmeans_clustered$genes$gene) %>%
right_join(gene_conv, .)
saveRDS(cov2_kmeans_clustered, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200802_k_means_clustered.rds")
saveRDS(cov2_kmeans_noclustering, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_k_means_unclustered.rds")
write.csv(cov2_kmeans_clustered$genes, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_k_means_genes.csv")
write.csv(cov2_kmeans_clustered$go_df, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_k_means_GO.csv")This is actually very cool (and nicely foreshadows the later WGCNA results). C1: mostly COVID-related(!); highly enriched for type I interferon response (not production so much), as well as MHC-I. C2: mostly nonviral pneumonias. Pretty classic STAT3 inflammatory response.
D0 Heatmap
d0_des = mp_des[, !is.na(mp_des$day_of_intubation) & mp_des$day_of_intubation <= 2 & mp_des$day_of_intubation >= 0]
# cov2_d0_kmeans_noclustering = k_means_figure(dge = d0_des,
# design = "~Diagnosis",
# k = 3,
# go_annotations = "org.Hs.eg.db",
# ensembl_db = "hsapiens_gene_ensembl",
# legend_factors = c("Diagnosis", "percent_total_CD206_high"),
# breaks = seq(-3, 3, length.out=101),
# cores = 12,
# minReps = Inf,
# return_genes = T,
# display_go_terms = F,
# return_go_terms = T,
# cluster_columns = F,
# label_fontsize = 2,
# customAnno = gene_conv,
# annoJoinCol = "ensembl_gene_id",
# sortColumns = T,
# colSortFactor = "Diagnosis",
# baseMeanCutoff = 20)
cov2_d0_kmeans_clustered = k_means_figure(dge = d0_des,
design = "~Diagnosis",
k = 3,
go_annotations = "org.Hs.eg.db",
ensembl_db = "hsapiens_gene_ensembl",
legend_factors = c("Diagnosis", "percent_total_CD206_high"),
breaks = seq(-3, 3, length.out=101),
cores = 12,
minReps = Inf,
return_genes = T,
display_go_terms = F,
return_go_terms = T,
cluster_columns = T,
label_fontsize = 2,
customAnno = gene_conv,
annoJoinCol = "ensembl_gene_id",
baseMeanCutoff = 20)Individual genes
CCL24
This was an interesting COVID-specific hit. According to published data, it is a highly selective chemoattractant for T cells. Would expect that it should correlate with T cell abundance.
ccl24_counts = plotCounts(mp_des,
gene = "ENSG00000106178",
intgroup = "Diagnosis",
normalized = T,
pc = T,
returnData = T)
ggplot(ccl24_counts, aes(x = Diagnosis, y = count, fill = Diagnosis)) +
geom_boxplot(width = 0.5, outlier.shape = NA) +
geom_jitter(size = 0.1, width = 0.25) +
scale_y_continuous(trans = "log10") +
scale_fill_manual(name = "",
values = c("Healthy_Control" = fig2_pal[6],
"Non_Pneumonia_Control" = fig2_pal[2],
"COVID_19" = fig2_pal[1],
"Other_Viral_Pneumonia" = fig2_pal[3],
"Other_Pneumonia" = fig2_pal[4])) +
scale_x_discrete(labels = c("Healthy_Control" = "Healthy\nControl",
"Non_Pneumonia_Control" = "Non-Pneumonia\nControl",
"COVID_19" = "COVID-19",
"Other_Viral_Pneumonia" = "Other Viral\nPneumonia",
"Other_Pneumonia" = "Other\nPneumonia")) +
xlab("") +
ylab("CCL24 Counts") +
theme_bw() +
theme(legend.position = "none",
text = element_text(size = 32, family = "Arial"),
plot.title = element_text(hjust = 0.5))
ccl24_cd4 = plotCounts(mp_des,
gene = "ENSG00000106178",
intgroup = "percent_CD4_total",
normalized = T,
returnData = T) %>%
rownames_to_column("sample")
ccl24_cd8 = plotCounts(mp_des,
gene = "ENSG00000106178",
intgroup = "percent_CD8_total",
normalized = T,
returnData = T) %>%
rownames_to_column("sample")
ccl24_tcells = left_join(ccl24_cd4, ccl24_cd8) %>%
pivot_longer(names_to = "flow_parameter",
values_to = "percent",
cols = c(percent_CD8_total, percent_CD4_total))Joining, by = c("sample", "count")ggplot(ccl24_tcells, aes(x = count, y = percent)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(~flow_parameter) +
scale_x_log10()
Pretty poor correlation
IL-6
il6_counts = plotCounts(mp_des,
gene = "ENSG00000136244",
intgroup = "Diagnosis",
normalized = T,
pc = T,
returnData = T)
ggplot(il6_counts, aes(x = Diagnosis, y = count, fill = Diagnosis)) +
geom_boxplot(width = 0.5, outlier.shape = NA) +
geom_jitter(size = 0.1, width = 0.25) +
scale_y_continuous(trans = "log10") +
scale_fill_manual(name = "",
values = c("Healthy_Control" = fig2_pal[6],
"Non_Pneumonia_Control" = fig2_pal[2],
"COVID_19" = fig2_pal[1],
"Other_Viral_Pneumonia" = fig2_pal[3],
"Other_Pneumonia" = fig2_pal[4])) +
scale_x_discrete(labels = c("Healthy_Control" = "Healthy\nControl",
"Non_Pneumonia_Control" = "Non-Pneumonia\nControl",
"COVID_19" = "COVID-19",
"Other_Viral_Pneumonia" = "Other Viral\nPneumonia",
"Other_Pneumonia" = "Other\nPneumonia")) +
xlab("") +
ylab("IL-6 Counts") +
theme_bw() +
theme(legend.position = "none",
text = element_text(size = 32, family = "Arial"),
plot.title = element_text(hjust = 0.5))
Sasha’s picks
All to start
sasha_genes = c("CCL20", "CCL24", "CCL3", "CCL8", "CCR2", "CCL2", "IL1A", "IL1B", "AREG",
"CXCR4", "TNFSF14", "CXCL3", "CXCL5", "CXCL1", "CXCL8", "ATF4", "CD164",
"MFGE8", "IL13RA1", "IL1RL1", "IL18R1", "VSIG4", "VEGFA", "DDIT4", "BAG3",
"DNAJB1", "HSP90AA6P", "HSPA1B", "HSPA1A")
sasha_genes = subset(gene_conv, gene_name %in% sasha_genes)
sasha_counts = mclapply(sasha_genes$ensembl_gene_id, function(x){
counts = plotCounts(mp_des,
gene = x,
intgroup = "Diagnosis",
returnData = T,
normalized = T,
pc = T)
counts$gene = x
return(counts)})
sasha_counts = bind_rows(sasha_counts) %>%
left_join(., sasha_genes, by = c("gene" = "ensembl_gene_id"))
sasha_gene_plot = ggplot(sasha_counts, aes(x = Diagnosis, y = count, fill = Diagnosis)) +
geom_boxplot(width = 0.5, outlier.shape = NA) +
geom_jitter(size = 0.1, width = 0.25) +
scale_y_continuous(trans = "log10") +
scale_fill_manual(name = "",
values = c("Healthy_Control" = fig2_pal[6],
"Non_Pneumonia_Control" = fig2_pal[2],
"COVID_19" = fig2_pal[1],
"Other_Viral_Pneumonia" = fig2_pal[3],
"Other_Pneumonia" = fig2_pal[4])) +
scale_x_discrete(labels = c("Healthy_Control" = "Healthy\nControl",
"Non_Pneumonia_Control" = "Non-Pneumonia\nControl",
"COVID_19" = "COVID-19",
"Other_Viral_Pneumonia" = "Other Viral\nPneumonia",
"Other_Pneumonia" = "Other\nPneumonia")) +
xlab("") +
ylab("Gene Counts") +
theme_bw() +
theme(legend.position = "none") +
facet_wrap(~gene_name)
sasha_gene_plot
Clustering
All samples
detection_rate = function(x)
{
return(sum(x > 0) / length(x))
}
all_counts = counts(mp_des, normalized = T)
#get 500 most variable genes with > 10% detection
rv = rowVars(all_counts)
prop_detected = apply(X = all_counts, MARGIN = 1, FUN = detection_rate)
selection_criteria = data.frame(sample = rownames(all_counts), var = rv, prop_detected = prop_detected) %>%
filter(prop_detected >= 0.1 & var > 0) %>%
arrange(desc(var))
top_genes = as.character(selection_criteria$sample[1:1000])
top_all_counts = all_counts[top_genes, ]
annotation = colData(mp_des) %>%
as.data.frame() %>%
dplyr::select(covid_confirmed,
sars_cov2_detected,
sex,
#day_of_intubation,
infection_type,
percent_neutrophils,
percent_total_CD206_high)
gene_annotation = gene_conv %>%
dplyr::filter(ensembl_gene_id %in% rownames(top_all_counts)) %>%
column_to_rownames(var = "ensembl_gene_id")
pheatmap(top_all_counts,
cluster_rows = T,
cluster_cols = T,
clustering_method = "ward.D2",
annotation_col = annotation,
labels_row = gene_annotation$gene_name,
scale = "row",
show_rownames = T,
fontsize_row = 2,
show_colnames = F,
breaks = seq(-3, 3, length.out=101),
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(100),
angle_col = 45)
Day 0
day0_samples = which(mp$day_of_intubation <= 2)
day0 = mp_des[, day0_samples]
day0_counts = counts(day0, normalized = T)
#get 1000 most variable genes
rv = rowVars(day0_counts)
prop_detected = apply(X = day0_counts, MARGIN = 1, FUN = detection_rate)
selection_criteria = data.frame(sample = rownames(day0_counts), var = rv, prop_detected = prop_detected) %>%
filter(prop_detected >= 0.1 & var > 0) %>%
arrange(desc(var))
top_genes = as.character(selection_criteria$sample[1:1000])
top_day0_counts = day0_counts[top_genes, ]
annotation = colData(day0) %>%
as.data.frame() %>%
dplyr::select(covid_confirmed,
sars_cov2_detected,
gender,
infection_type,
percent_neutrophils,
percent_total_CD206_high)
gene_annotation = gene_conv %>%
dplyr::filter(ensembl_gene_id %in% rownames(top_day0_counts)) %>%
column_to_rownames(var = "ensembl_gene_id")
pheatmap(top_day0_counts,
cluster_rows = T,
cluster_cols = T,
clustering_method = "ward.D2",
annotation_col = annotation,
labels_row = gene_annotation$gene_name,
scale = "row",
show_rownames = T,
show_colnames = F,
fontsize_row = 2,
breaks = seq(-3, 3, length.out=101),
color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(100),
angle_col = 45)Comparing different pathogens
Individual pathogens
pathogen_set = mp_des[, !is.na(mp$pathogen)]
pathogen_set$pathogen = factor(as.character(pathogen_set$pathogen)) #refactor
pathogen_kmeans = k_means_figure(dge = pathogen_set,
design = "~pathogen",
k = 6,
go_annotations = "org.Hs.eg.db",
ensembl_db = "hsapiens_gene_ensembl",
legend_factors = c("pathogen", "covid_confirmed"),
breaks = seq(-3, 3, length.out=101),
cores = 12)Probably too granular
Broad groupings
infection_type_kmeans = k_means_figure(dge = pathogen_set,
design = "~infection_type",
k = 4,
go_annotations = "org.Hs.eg.db",
ensembl_db = "hsapiens_gene_ensembl",
legend_factors = c("pathogen", "infection_type"),
breaks = seq(-3, 3, length.out=101),
cores = 12)Random
ACE2 counts
ace2_counts = plotCounts(mp_des,
gene = "ENSG00000130234",
intgroup = "covid_confirmed",
returnData = T,
normalized = T,
pc = F)
ggplot(ace2_counts, aes(x = count)) +
geom_histogram(fill = "dodgerblue4", ) +
ylab("Number of Samples") +
xlab("ACE2 Count")
Trajectories?
All samples
We will use PCA as is recommended to make computation feasible.
#select genes with > 10% detection, mean counts > 5
all_counts = t(counts(mp_des, normalized = T)) #need size normalization
prop_detected = apply(X = all_counts, MARGIN = 2, FUN = detection_rate)
all_counts = all_counts[, prop_detected > 0.1]
mean_detection = colMeans(all_counts)
all_counts = all_counts[, mean_detection >= 5]
all_pca = prcomp(all_counts)
fviz_eig(all_pca,
barfill = "dodgerblue4",
xlab = "Principal Component",
ylab = "Percent Variance Explained",
main = "",
ncp = 50,
ggtheme = theme_bw(),
addlabels = T) #20 should capture almost everything
total_umap = umap(X = all_counts,
pca = 20,
pca_center = T,
n_threads = 1,
scale = "Z",
min_dist = 0.2)
rownames(total_umap) = rownames(all_counts)
colnames(total_umap) = c("UMAP_1", "UMAP_2")
total_umap = as.data.frame(total_umap)
total_umap$sample = rownames(total_umap)
total_umap = left_join(total_umap, metadata, by = "sample") %>%
arrange(study_id, day_of_intubation) # so we can draw paths
ggplot(total_umap, aes(x = UMAP_1, y = UMAP_2, color = Diagnosis)) +
geom_point() +
stat_ellipse()
ggplot(total_umap, aes(x = UMAP_1, y = UMAP_2, shape = study_id, color = day_of_intubation)) +
geom_path(arrow = grid::arrow()) +
scale_color_viridis() +
facet_wrap(~ Diagnosis)
Patients actually separate out really well here. This might be worth including as a supplement. With that said, probably better to just use COVID patients for “trajectories”. It looks like there may be some structure to the COVID data.
Only COVID
#remove non-detected genes
#note that PCA should only use genes detected in all samples (3081)
COVID_counts = t(counts(mp_des[, mp_des$Diagnosis == "COVID_19"], normalized = T)) #need size normalization
prop_detected = apply(X = COVID_counts, MARGIN = 2, FUN = detection_rate)
COVID_counts = COVID_counts[, prop_detected > 0.1]
mean_detection = colMeans(COVID_counts)
COVID_counts = COVID_counts[, mean_detection >= 5]
COVID_pca = prcomp(COVID_counts)
fviz_eig(COVID_pca,
barfill = "dodgerblue4",
xlab = "Principal Component",
ylab = "Percent Variance Explained",
main = "",
ncp = 50,
ggtheme = theme_bw(),
addlabels = T) #20 should capture almost everything
COVID_umap = umap(X = COVID_counts,
pca = 20,
pca_center = T,
n_threads = 1,
scale = "Z",
min_dist = 0.4)
rownames(COVID_umap) = rownames(COVID_counts)
colnames(COVID_umap) = c("UMAP_1", "UMAP_2")
COVID_umap = as.data.frame(COVID_umap)
COVID_umap$sample = rownames(COVID_umap)
COVID_umap = left_join(COVID_umap, metadata, by = "sample") %>%
arrange(study_id, day_of_intubation) # so we can draw paths
ggplot(COVID_umap, aes(x = UMAP_1, y = UMAP_2, color = day_of_intubation)) +
scale_color_viridis() +
geom_point()
ggplot(COVID_umap, aes(x = UMAP_1, y = UMAP_2, shape = study_id, color = day_of_intubation)) +
scale_color_viridis() +
geom_path(arrow = grid::arrow())
Not so much structure it seems.
WGCNA
What gene modules are associated with covid? Which change substantially over time on ventilator?
## Thesholding
#setup
enableWGCNAThreads(nThreads = 12)Allowing parallel execution with up to 12 working processes.WGCNAnThreads()[1] 12# Choose a set of soft-thresholding powers
powers = c(1:20)
# Call the network topology analysis functions
sft = pickSoftThreshold(all_counts, powerVector = powers, verbose = 5, networkType = "signed")pickSoftThreshold: will use block size 3395.
pickSoftThreshold: calculating connectivity for given powers...
..working on genes 1 through 3395 of 13176
..working on genes 3396 through 6790 of 13176
..working on genes 6791 through 10185 of 13176
..working on genes 10186 through 13176 of 13176# Plot the results:
sizeGrWindow(9, 5)
par(mfrow = c(1,2))
cex1 = 0.9
# Scale-free topology fit index as a function of the soft-thresholding power
plot(sft$fitIndices[,1],
-sign(sft$fitIndices[,3])*sft$fitIndices[,2],
xlab="Soft Threshold (power)",
ylab="Scale Free Topology Model Fit,signed R^2",
type="n",
main = paste("Scale independence"))
text(sft$fitIndices[,1],
-sign(sft$fitIndices[,3])*sft$fitIndices[,2],
labels=powers,
cex=cex1,
col="red")
# this line corresponds to using an R^2 cut-off of h
abline(h=0.90,col="red")
# Mean connectivity as a function of the soft-thresholding power
plot(sft$fitIndices[,1],
sft$fitIndices[,5],
xlab="Soft Threshold (power)",
ylab="Mean Connectivity",
type="n",
main = paste("Mean connectivity"))text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red")
7 looks like the intersection after adding healthy controls
Co-expression by similarity and adjacency
softPower = 7
adjacency = adjacency(all_counts, power = softPower, type = "signed")Topological overlap matrix
# Turn adjacency into topological overlap
TOM = TOMsimilarity(adjacency, TOMType = "signed")..connectivity..
..matrix multiplication (system BLAS)..
..normalization..
..done.Cluster by TOM distance
geneTree = hclust(as.dist(dissTOM), method = "average")Create modules
minModuleSize = 30 # may need to adjust
# Make modules based on clusters
dynamicMods = cutreeDynamic(dendro = geneTree,
distM = dissTOM,
deepSplit = 2,
pamRespectsDendro = FALSE,
minClusterSize = minModuleSize) ..cutHeight not given, setting it to 0.989 ===> 99% of the (truncated) height range in dendro.
..done.table(dynamicMods)dynamicMods
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
2734 2473 2350 1019 830 711 697 647 370 293 273 271 246 143 119 Label modules (yeah this is dumb – rename later)
# Convert numeric labels into colors
dynamicColors = labels2colors(dynamicMods)
table(dynamicColors)dynamicColors
black blue brown cyan green greenyellow magenta midnightblue pink
697 2473 2350 143 830 273 370 119 647
purple red salmon tan turquoise yellow
293 711 246 271 2734 1019 # Plot the dendrogram and colors underneath
sizeGrWindow(8,6)
plotDendroAndColors(geneTree,
dynamicColors,
"Dynamic Tree Cut",
dendroLabels = FALSE,
hang = 0.03,
addGuide = TRUE,
guideHang = 0.05,
main = "Gene dendrogram and module colors")
Looks like we captured the structure pretty well.
Merge similar modules
# Calculate eigengenes
MEList = moduleEigengenes(all_counts,
colors = dynamicColors)
MEs = MEList$eigengenes
# Calculate dissimilarity of module eigengenes
MEDiss = 1-cor(MEs)
# Cluster module eigengenes
METree = hclust(as.dist(MEDiss),
method = "average")
# Plot the result
sizeGrWindow(7, 6)
plot(METree,
main = "Clustering of module eigengenes",
xlab = "",
sub = "")
#Now cut at height of 0.5 to get correlation of 0.5
#standard is 0.25, but we have some really similar modules that are not relevant
MEDissThres = 0.25
# Plot the cut line into the dendrogram
abline(h=MEDissThres,
col = "red")
# Call an automatic merging function
merge = mergeCloseModules(all_counts,
dynamicColors,
cutHeight = MEDissThres,
verbose = 3) mergeCloseModules: Merging modules whose distance is less than 0.25
multiSetMEs: Calculating module MEs.
Working on set 1 ...
moduleEigengenes: Calculating 15 module eigengenes in given set.
Calculating new MEs...
multiSetMEs: Calculating module MEs.
Working on set 1 ...
moduleEigengenes: Calculating 15 module eigengenes in given set.# The merged module colors
mergedColors = merge$colors
# Eigengenes of the new merged modules:
mergedMEs = merge$newMEs
# Rename to moduleColors
moduleColors = mergedColors
# Construct numerical labels corresponding to the colors
colorOrder = c("grey", standardColors(50))
moduleLabels = match(moduleColors, colorOrder)-1
MEs = mergedMEs
#replot
sizeGrWindow(12, 9)
plotDendroAndColors(geneTree,
cbind(dynamicColors, mergedColors),
c("Dynamic Tree Cut", "Merged dynamic"),
dendroLabels = FALSE,
hang = 0.03,
addGuide = TRUE,
guideHang = 0.05)
No change
Relate back to traits
Without vent settings
# Define numbers of genes and samples
nGenes = ncol(all_counts)
nSamples = nrow(all_counts)
metadata = as.data.frame(colData(mp_des))
md_of_interest = metadata %>%
mutate(deceased = ifelse(binned_outcome == "Deceased",
yes = 1,
no = 0)) %>%
mutate(has_covid = ifelse(covid_confirmed == "TRUE",
yes = 1,
no = 0)) %>%
mutate(cov2_detected = ifelse(sars_cov2_detected == TRUE,
yes = 1,
no = 0)) %>%
mutate(has_pneumonia = ifelse(pna_type == "Non-Pneumonia Control",
yes = 0,
no = 1)) %>%
mutate(coinfection_detected = ifelse(any_nonviral == "TRUE",
yes = 1,
no = 0)) %>%
mutate(other_viral_pna = ifelse(pna_type == "Other Viral Pneumonia",
yes = 1,
no = 0)) %>%
mutate(nonviral_pna = ifelse(pna_type == "Other Pneumonia",
yes = 1,
no = 0)) %>%
mutate(is_healthy_control = ifelse(pna_type == "Healthy Control",
yes = 1,
no = 0)) %>%
dplyr::select(deceased, has_covid, cov2_detected, has_pneumonia,
other_viral_pna, nonviral_pna, is_healthy_control,
percent_neutrophils, percent_total_CD206_high,
percent_CD4_total, percent_CD8_total, finite_day_of_intubation,
C_Reactive_Protein, D_DIMER, PROCALCITONIN, mean_aps)
# Recalculate MEs with color labels
MEs0 = moduleEigengenes(all_counts, moduleColors)$eigengenes
MEs = orderMEs(MEs0)
moduleTraitCor = bicor(MEs,
md_of_interest,
use = "pairwise.complete.obs",
maxPOutliers = 0.05)bicor: zero MAD in variable 'y'. Pearson correlation was used for individual columns with zero (or missing) MAD.moduleTraitPvalue = corPvalueStudent(moduleTraitCor,
nSamples) %>%
as.numeric() %>%
p.adjust(., method = "fdr") %>%
matrix(ncol = ncol(moduleTraitCor))
colnames(moduleTraitPvalue) = colnames(moduleTraitCor)
rownames(moduleTraitPvalue) = rownames(moduleTraitCor)
moduleTraitCor_hm = t(moduleTraitCor)
#just make significant or not
pvals_hm = t(moduleTraitPvalue) %>%
as.matrix()
pvals_hm[pvals_hm < 0.05] = ""
pvals_hm[pvals_hm != ""] = "NS"
labs = c("Deceased", "COVID-19", "SARS-CoV-2 Detected", "Any Pneumonia", "Other Viral Pneumonia", "Other Pneumonia",
"Healthy Control", "% Neutrophils", "% Macrophages CD206-high", "% CD4 T", "% CD8 T",
"Day of Intubation", "CRP", "D-Dimer", "Procalcitonin", "Mean APS")
module_cor_heatmap = pheatmap(mat = moduleTraitCor_hm,
labels_row = labs,
labels_col = paste("Module", c(1:ncol(MEs))),
display_numbers = pvals_hm,
cluster_rows = T,
cluster_cols = T,
clustering_method = "ward.D2",
color = colorRampPalette(rev(brewer.pal(n = 7,
name = "RdBu")))(100),
angle_col = 315)
module_cor_heatmap = pheatmap(mat = moduleTraitCor_hm,
labels_row = labs,
labels_col = paste("Module", c(1:ncol(MEs))),
display_numbers = pvals_hm,
cluster_rows = T,
cluster_cols = T,
clustering_method = "ward.D2",
color = colorRampPalette(rev(brewer.pal(n = 7,
name = "RdBu")))(100),
angle_col = 315,
fontsize = 32)
Module 12 (turquoise) looks like the key MoAM cluster, 4 (blue) is key TRAM, and module 14 (purple) is likely our IFN cluster.
With vent settings
# Define numbers of genes and samples
nGenes = ncol(all_counts)
nSamples = nrow(all_counts)
md_of_interest = metadata %>%
mutate(deceased = ifelse(binned_outcome == "Deceased",
yes = 1,
no = 0)) %>%
mutate(has_covid = ifelse(covid_confirmed == "TRUE",
yes = 1,
no = 0)) %>%
mutate(cov2_detected = ifelse(sars_cov2_detected == TRUE,
yes = 1,
no = 0)) %>%
mutate(has_pneumonia = ifelse(pna_type == "Non-Pneumonia Control",
yes = 0,
no = 1)) %>%
mutate(coinfection_detected = ifelse(any_nonviral == "TRUE",
yes = 1,
no = 0)) %>%
mutate(other_viral_pna = ifelse(pna_type == "Other Viral Pneumonia",
yes = 1,
no = 0)) %>%
mutate(nonviral_pna = ifelse(pna_type == "Other Pneumonia",
yes = 1,
no = 0)) %>%
mutate(is_healthy_control = ifelse(pna_type == "Healthy Control",
yes = 1,
no = 0)) %>%
dplyr::select(deceased, has_covid, cov2_detected, has_pneumonia,
other_viral_pna, nonviral_pna, is_healthy_control,
percent_neutrophils, percent_total_CD206_high,
percent_CD4_total, percent_CD8_total, finite_day_of_intubation,
C_Reactive_Protein, D_DIMER, PROCALCITONIN, mean_aps,
Static_Compliance, PF_ratio)
# Recalculate MEs with color labels
MEs0 = moduleEigengenes(all_counts, moduleColors)$eigengenes
MEs = orderMEs(MEs0)
moduleTraitCor = bicor(MEs,
md_of_interest,
use = "pairwise.complete.obs",
maxPOutliers = 0.05)bicor: zero MAD in variable 'y'. Pearson correlation was used for individual columns with zero (or missing) MAD.moduleTraitPvalue = corPvalueStudent(moduleTraitCor,
nSamples) %>%
as.numeric() %>%
p.adjust(., method = "fdr") %>%
matrix(ncol = ncol(moduleTraitCor))
colnames(moduleTraitPvalue) = colnames(moduleTraitCor)
rownames(moduleTraitPvalue) = rownames(moduleTraitCor)
moduleTraitCor_hm = t(moduleTraitCor)
#just make significant or not
pvals_hm = t(moduleTraitPvalue) %>%
as.matrix()
pvals_hm[pvals_hm < 0.05] = ""
pvals_hm[pvals_hm != ""] = "NS"
labs = c("Deceased", "COVID-19", "SARS-CoV-2 Detected", "Any Pneumonia", "Other Viral Pneumonia", "Other Pneumonia",
"Healthy Control", "% Neutrophils", "% Macrophages CD206-high", "% CD4 T", "% CD8 T",
"Day of Intubation", "CRP", "D-Dimer", "Procalcitonin", "Mean APS",
"Static Compliance", "P/F Ratio")
module_cor_heatmap_vent_mini = pheatmap(mat = moduleTraitCor_hm,
labels_row = labs,
labels_col = paste("Module", c(1:ncol(MEs))),
display_numbers = pvals_hm,
cluster_rows = T,
cluster_cols = T,
clustering_method = "ward.D2",
color = colorRampPalette(rev(brewer.pal(n = 7,
name = "RdBu")))(100),
angle_col = 315)
module_cor_heatmap_vent = pheatmap(mat = moduleTraitCor_hm,
labels_row = labs,
labels_col = paste("Module", c(1:ncol(MEs))),
display_numbers = pvals_hm,
cluster_rows = T,
cluster_cols = T,
clustering_method = "ward.D2",
color = colorRampPalette(rev(brewer.pal(n = 7,
name = "RdBu")))(100),
angle_col = 315,
fontsize = 24)
saveRDS(module_cor_heatmap_vent, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200803_WGCNA_cor_heatmap.rds")Module 13 (salmon) may also be interesting. Correlates with COVID, CRP, lymphocytes, and vent params!
Sanity check: are the CD206 modules TRAM/MoAM markers?
module_assignments = data.frame(ensembl_gene_id = colnames(all_counts),
module = factor(mergedColors)) %>%
left_join(., gene_conv)Joining, by = "ensembl_gene_id"write.csv(module_assignments,
"/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_WGCNA_module_assignments.csv")
cd206_correlated = subset(module_assignments, module == "blue")
cd206_anticorrelated = subset(module_assignments, module == "turquoise")
cd206_correlatedcd206_anticorrelatedPretty ideal, yeah
COVID-associated
Purple contains all of the CoV-2 genes!
covid_correlated_cd206_uncorrelated = subset(module_assignments, module == "purple")
covid_correlated_cd206_uncorrelatedwrite.csv(covid_correlated_cd206_uncorrelated,
"/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_purple_module_14_genes.csv")Pretty classic interferon response. Look at the GO.
#from k_means_figure
complete_counts = counts(mp_des, normalized = T)
universe = rownames(complete_counts[rowSums(complete_counts) > 0, ])
fisherTest = new("classicCount", testStatistic = GOFisherTest, name = "Fisher test")
cluster_genes = covid_correlated_cd206_uncorrelated$ensembl_gene_id
selection = as.numeric(universe %in% cluster_genes)
names(selection) = universe
go_data = new("topGOdata",
ontology = "BP",
allGenes = selection,
geneSel = function(x){
return(x == 1)},
annot = annFUN.org,
mapping = "org.Hs.eg.db",
ID = "ensembl")
Building most specific GOs .....
( 12142 GO terms found. )
Build GO DAG topology ..........
( 16096 GO terms and 38209 relations. )
Annotating nodes ...............
( 16536 genes annotated to the GO terms. )
#run Fisher test
test_results = getSigGroups(go_data, fisherTest)
-- Classic Algorithm --
the algorithm is scoring 3412 nontrivial nodes
parameters:
test statistic: Fisher testmodule14_score = as.data.frame(score(test_results))
colnames(module14_score) = "pval"
module14_score = rownames_to_column(module14_score, var = "go_id")
#adjust p-values and take significant
module14_score$padj = p.adjust(module14_score$pval, method = "fdr")
module14_score = subset(module14_score, padj < 0.05)
module14_score$description = NA
for(i in 1:nrow(module14_score))
{
module14_score$description[i] = GOTERM[[module14_score$go_id[i]]]@Term
}
#add descriptions
module14_score$full_go = paste(module14_score$go_id, module14_score$description)
write.csv(module14_score,
"/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_purple_module_14_GO.csv")
module14_scorePretty much all Type I interferon related. Most of the paper is here.
Module 13
This module has IRF1 and the MHC-I genes
module13 = subset(module_assignments, module == "salmon")
module13write.csv(module13,
"/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_salmon_module_13_genes.csv")#from k_means_figure
cluster_genes = module13$ensembl_gene_id
selection = as.numeric(universe %in% cluster_genes)
names(selection) = universe
go_data = new("topGOdata",
ontology = "BP",
allGenes = selection,
geneSel = function(x){
return(x == 1)},
annot = annFUN.org,
mapping = "org.Hs.eg.db",
ID = "ensembl")
Building most specific GOs .....
( 12142 GO terms found. )
Build GO DAG topology ..........
( 16096 GO terms and 38209 relations. )
Annotating nodes ...............
( 16536 genes annotated to the GO terms. )
#run Fisher test
test_results = getSigGroups(go_data, fisherTest)
-- Classic Algorithm --
the algorithm is scoring 2785 nontrivial nodes
parameters:
test statistic: Fisher testmodule13_score = as.data.frame(score(test_results))
colnames(module13_score) = "pval"
module13_score = rownames_to_column(module13_score, var = "go_id")
#adjust p-values and take significant
module13_score$padj = p.adjust(module13_score$pval, method = "fdr")
module13_score = subset(module13_score, padj < 0.05)
# module13_score$description = NA
# for(i in 1:nrow(module13_score))
# {
# module13_score$description[i] = GOTERM[[module13_score$go_id[i]]]@Term
# }
#
# #add descriptions
# module13_score$full_go = paste(module13_score$go_id, module13_score$description)
# write.csv(module13_score,
# "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_salmon_module_13_GO.csv")
# module13_scoreNo significant hits.
Plot interesting modules
Purple module (14) and diagnosis, outcome
CCL24
ccl24_counts = rownames_to_column(ccl24_counts, var = "sample")
ccl24_counts = ccl24_counts[, 1:2]
colnames(ccl24_counts)[2] = "CCL24"
total_umap = left_join(total_umap, ccl24_counts)Joining, by = "sample"ccl24_umap = ggplot(total_umap, aes(x = UMAP_1, y = UMAP_2, size = CCL24, color = pna_type, shape = binned_outcome)) +
geom_point() +
theme_bw() +
theme(text = element_text(size = 16, family = "Arial"),
legend.text = element_text(size = 16, family = "Arial"),
legend.title = element_text(size = 16, family = "Arial")) +
scale_color_manual(name = "",
values = c("Healthy_Control" = fig2_pal[6],
"Non-Pneumonia Control" = fig2_pal[2],
"COVID-19" = fig2_pal[1],
"Other Viral Pneumonia" = fig2_pal[3],
"Other Pneumonia" = fig2_pal[4]),
na.value = "black") +
scale_shape_manual(name = "",
values = c("Deceased" = 13,
"Discharged" = 19,
"Inpatient Facility" = 18,
"Other" = 18),
na.value = 1) +
scale_size_continuous(name = "CCL24 Counts", trans = "log10", range = c(1, 10)) +
xlab("UMAP 1") +
ylab("UMAP 2")
ccl24_umap 
This is really cool! This isolates most the the IFN-non-responsive COVID patients. Maybe this drives heterogeneity in celltype composition in the lung?
tcell_umap = ggplot(total_umap, aes(x = UMAP_1, y = UMAP_2, size = percent_CD3_total, color = pna_type, shape = binned_outcome)) +
geom_point() +
theme_bw() +
theme(text = element_text(size = 16, family = "Arial"),
legend.text = element_text(size = 16, family = "Arial"),
legend.title = element_text(size = 16, family = "Arial")) +
scale_color_manual(name = "",
values = c("Healthy_Control" = fig2_pal[6],
"Non-Pneumonia Control" = fig2_pal[2],
"COVID-19" = fig2_pal[1],
"Other Viral Pneumonia" = fig2_pal[3],
"Other Pneumonia" = fig2_pal[4]),
na.value = "black") +
scale_shape_manual(name = "",
values = c("Deceased" = 13,
"Discharged" = 19,
"Inpatient Facility" = 18,
"Other" = 18),
na.value = 1) +
scale_size_continuous(name = "Percent Lymphocytes", range = c(1, 10)) +
xlab("UMAP 1") +
ylab("UMAP 2")
tcell_umap 
Brown/turquoise modules and day of intubation in COVID patients
TRAM content
ggplot(total_umap, aes(x = day_of_intubation, y = AEblue)) +
geom_point() +
geom_smooth()
ggplot(total_umap, aes(x = day_of_intubation, y = AEturquoise)) +
geom_point() +
geom_smooth()
Not much of a correlation…maybe a flat line, as I guess would be expected with near-constant recruitment.
Effect of ORF8 expression
Isolate counts
orf8 = cov2_genes$ensembl_gene_id[which(cov2_genes$gene_name == "ORF8")]
orf8_counts = plotCounts(mp_des,
gene = orf8,
intgroup = "pna_type",
returnData = T,
normalized = T,
pc = T)
orf8_samples = rownames(subset(orf8_counts, count >0))
ggplot(orf8_counts, aes(x = pna_type, y = count)) +
geom_boxplot() +
geom_jitter(aes(color = count > 0)) +
scale_y_log10() +
ylab("ORF8 Counts") +
xlab("") 
Not a ton of detection but very clean.
Association with interferon responses
mp_des$orf8_detected = mp_des$sample %in% orf8_samples
total_umap$orf8_detected = total_umap$sample %in% orf8_samples
ggplot(total_umap, aes(x = UMAP_1, y = UMAP_2, size = AEpurple, color = Diagnosis, shape = orf8_detected)) +
geom_point()
Pretty clear spatial correlation…but in the opposite direction?
ggplot(total_umap, aes(x = orf8_detected, y = AEpurple)) +
geom_boxplot() +
geom_jitter(aes(color = Diagnosis))
high_cov2 = colnames(cov2_counts)[colSums(cov2_counts) > 50]
total_umap$cov2_detected = total_umap$sample %in% high_cov2
ggplot(total_umap, aes(x = cov2_detected, y = AEpurple)) +
geom_boxplot() +
geom_jitter(aes(color = Diagnosis))
This really just correlates with overall viral load (which correlates, in turn, with ORF8 levels). No support really for the ORF8 inhibition of the IFN response, at least at this stage.
Interferon response over time
WGCNA Module purple
Average expression
GO Categories
Pull from biomart
human_mart = useEnsembl("ensembl", dataset = "hsapiens_gene_ensembl", GRCh = 37)
#now pull down genes based on gene ontology (GO) definitions
typeI_IFN_response = getBM(attributes = c("ensembl_gene_id", "external_gene_name"),
filters = "go",
values = "GO:0060337",
mart = human_mart)`select_()` is deprecated as of dplyr 0.7.0.
Please use `select()` instead.
[90mThis warning is displayed once every 8 hours.[39m
[90mCall `lifecycle::last_warnings()` to see where this warning was generated.[39m`filter_()` is deprecated as of dplyr 0.7.0.
Please use `filter()` instead.
See vignette('programming') for more help
[90mThis warning is displayed once every 8 hours.[39m
[90mCall `lifecycle::last_warnings()` to see where this warning was generated.[39mCache foundIFNg_response = getBM(attributes = c("ensembl_gene_id", "external_gene_name"),
filters = "go",
values = "GO:0060333",
mart = human_mart)Cache foundAssign mean expression
covid_umap = covid_umap %>%
left_join(., type1_counts) %>%
left_join(., type2_counts) %>%
left_join(., ifn_counts)
Joining, by = "sample"
Joining, by = "sample"
Joining, by = "sample"Correlations
typei_cor = cor.test(covid_umap$typeI_IFN_response, covid_umap$finite_day_of_intubation)
typeii_cor = cor.test(covid_umap$IFNg_response, covid_umap$finite_day_of_intubation)
general_cor = cor.test(covid_umap$general_IFN_response, covid_umap$finite_day_of_intubation)Plot
Second batch of pico optimization
Let’s focus on interferon-hi vs interferon-low samples
### Identify interesting
last_batch = c(300312389, 304012699, 304017553, 304020298, 304020362,
340224808, 340233896, 340239789, 340239790, 340239805)
good_samples = mp_des[, ((!is.na(mp_des$RIN) & mp_des$RIN >= 7) &
mp_des$STAR_mqc_generalstats_star_uniquely_mapped_percent >= 30 &
mp_des$featureCounts_mqc_generalstats_featurecounts_percent_assigned >= 30) &
mp_des$RNA_concentration_pg_ul >= 125]
remaining_interesting = setdiff(good_samples$sample, last_batch)
interesting_metadata = subset(total_umap, sample %in% remaining_interesting)
table(interesting_metadata$pna_type)
ggplot(interesting_metadata, aes(x = pna_type, y = AEpurple)) +
geom_boxplot()
selection = interesting_metadata %>%
filter(pna_type != "Other Pneumonia" | sample == 340235110) %>%
dplyr::select(sample, tube_loc_macrophRNA_boxID, RNA_concentration_pg_ul) %>%
mutate(dilution = ifelse((250 / RNA_concentration_pg_ul) < 0.4,
yes = round(1 / (250 / RNA_concentration_pg_ul) / 5) * 5,
no = 1)) %>%
mutate(vol_for_250_pg = round(250 / RNA_concentration_pg_ul * dilution, digits = 2))
write.csv(selection, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200713_pico_opt_batch2.csv")
selectionConclusions
Output data for posterity
final_counts_raw = counts(mp_des, normalized = F)
#clean metadata for safe sharing
final_md_safe = colData(mp_des) %>%
as.data.frame() %>%
dplyr::select(sample, age, sex, diagnosis = pna_type,
day_of_ventilation = finite_day_of_intubation, study_id)
#convert script IDs to new identifiers
id_conv = data.frame(script_id = sample(unique(final_md_safe$study_id))) %>% #shuffle IDs first
mutate(anonymized_patient_id = sprintf("%04d", 1:nrow(id_conv)))
#add back to md
final_md_safe = left_join(final_md_safe, id_conv, by = c("study_id" = "script_id")) %>%
dplyr::select(-study_id)
write.csv(final_counts_raw, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/GEO_output/200724_raw_counts_geo.csv")
write.csv(final_md_safe, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/GEO_output/200724_deidentified_metadata_geo.csv")Export supplemental files
DEA
all_comparisons = combn(levels(mp_des$Diagnosis), 2, simplify = F)
names(all_comparisons) = vapply(all_comparisons, function(x){
return(paste0(x[1], " vs ", x[2]))}, "char")
dea_hits = lapply(all_comparisons, function(x){
hits = as.data.frame(results(object = mp_des,
contrast = c("Diagnosis", x[1], x[2]),
alpha = 0.05,
parallel = T)) %>%
rownames_to_column("ensembl_gene_id") %>%
left_join(., gene_conv) %>%
arrange(padj)
return(hits)})
names(dea_hits) = names(all_comparisons)
saveRDS(dea_hits, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/GEO_output/200728_bulk_dea_complete.rds")Export as single excel file
output_genes = cov2_kmeans_clustered$genes %>%
dplyr::select(ensembl_gene_id = gene, cluster, external_gene_name) %>%
arrange(cluster)
names(dea_hits) = gsub("Healthy_Control", "HC", names(dea_hits))
names(dea_hits) = gsub("Other_Viral_Pneumonia", "OVP", names(dea_hits))
names(dea_hits) = gsub("Other_Pneumonia", "OP", names(dea_hits))
names(dea_hits) = gsub("Non_Pneumonia_Control", "NPC", names(dea_hits))
names(dea_hits) = gsub("COVID_19", "COVID-19", names(dea_hits))
all_sheets = c(list("k-means genes" = output_genes,
"k-means GO enrichment" = cov2_kmeans_clustered$go_df),
dea_hits)
write.xlsx(x = all_sheets,
file = "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200728_bulk_supplmental_data.xlsx")Assemble figures
Figure 3
fig3_heatmap = cov2_kmeans_clustered$plot
fig3_heatmap_gg = as.ggplot(fig3_heatmap)
viral_expression_heatmap_gg = as.ggplot(viral_expression_heatmap)
module_cor_heatmap_vent_gg = as.ggplot(module_cor_heatmap_vent)
Error in as.grob(plot) : object 'module_cor_heatmap_vent' not foundFigure S3
Marker genes
marker_gene_plot = ggplot(s3_counts, aes(x = Diagnosis, y = count, fill = Diagnosis)) +
geom_boxplot(width = 0.5, outlier.shape = NA) +
geom_jitter(width = 0.25) +
scale_y_continuous(trans = "log10") +
scale_fill_manual(name = "",
values = c("Healthy_Control" = fig2_pal[6],
"Non_Pneumonia_Control" = fig2_pal[2],
"COVID_19" = fig2_pal[1],
"Other_Viral_Pneumonia" = fig2_pal[3],
"Other_Pneumonia" = fig2_pal[4])) +
scale_x_discrete(labels = c("Healthy_Control" = "Healthy\nControl",
"Non_Pneumonia_Control" = "Non-Pneumonia\nControl",
"COVID_19" = "COVID-19",
"Other_Viral_Pneumonia" = "Other Viral\nPneumonia",
"Other_Pneumonia" = "Other\nPneumonia")) +
xlab("") +
ylab("Gene Counts") +
facet_wrap(~gene_name, scale = "free_y", ncol = 3) +
geom_signif(inherit.aes = F,
data = gene_comps,
aes(xmin = numerator, xmax = denominator, annotations = label, y_position = y_log10),
tip_length = 0,
manual=TRUE,
family = "Arial",
textsize = 4) +
theme_bw(base_family = "Arial", base_size = 32) +
theme(legend.position = "none",
text = element_text(family = "Arial"),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
plot.title = element_text(hjust = 0.5),
strip.background = element_blank())
Ignoring unknown aesthetics: xmin, xmax, annotations, y_positioncov2_kmeans_noclustering = readRDS("/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_k_means_unclustered.rds")
coverage_plot = readRDS("/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200803_cov2_coverage_plot.rds")
figS3_heatmap = cov2_kmeans_noclustering$plot
figS3_heatmap_gg = as.ggplot(figS3_heatmap)
layout = "ABB\nABB\nABB\nABB\n#BB\nCBB\nDBB"
bulk_plot_fig3 = figS3_heatmap_gg +
marker_gene_plot +
sars_time_cor +
coverage_plot +
plot_layout(design = layout) +
plot_annotation(tag_levels = 'a') & theme(plot.tag = element_text(size = 48))
bulk_plot_fig3Figure S3
1[1] 1md = as.data.frame(colData(mp_des))
col_types = unlist(lapply(md, class))
list_cols = names(col_types[col_types == "AsIs"])
safe_md = md %>%
dplyr::select(-c(all_of(list_cols))) %>%
as.data.frame()
write.csv(safe_md, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200730_bulk_mp_metadata.csv")---
title: "SCRIPT Bulk High-Level Analysis"
output: html_notebook
---

# Goals   
1. Identify transcriptional patterns in AM associated with IAV, COVID-19, and common to both   
2. Determine source of IL6 and IL1B in the lung (if possible)   
   
# Setup   
## Load packages   
```{r setup}
library(ggplot2)
library(ggplotify)
library(ggsignif)
library(Cairo)
library(DESeq2)
library(pheatmap)
library(RColorBrewer)
library(viridis)
library(patchwork)
library(factoextra)
library(parallel)
library(uwot)
library(WGCNA)
library(ggsci)
library(topGO)
library(GO.db)
library(org.Hs.eg.db)
library(parallel)
library(doParallel)
library(biomaRt)
library(openxlsx)
registerDoParallel(makeCluster(12))
register(MulticoreParam(12))
options(gsubfn.engine = "R")
set.seed(12345)
source("~/utils/R/pretty_MA_plot.R")
source("~/utils/R/k_means_figure.R")
source("~/utils/R/plotPCA_manual.R")
fig2_pal = pal_npg("nrc")(9)

sessionInfo()
```

## Import data   
```{r}
mp = readRDS("/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_script_bulk_des_no_hURNA.rds")
mp$covid_confirmed = factor(mp$covid_confirmed)
mp$sex = factor(mp$gender)

#set up for comparison by diagnosis
mp$Diagnosis = gsub("-| ", "_", as.character(mp$pna_type))
mp$Diagnosis = factor(mp$Diagnosis, 
                      levels = c("Healthy_Control", "Non_Pneumonia_Control", "COVID_19", 
                                 "Other_Viral_Pneumonia", "Other_Pneumonia"))
mp$neutrophilic = mp$percent_neutrophils > 50
#for modeling, etc
mp$finite_day_of_intubation = mp$day_of_intubation
mp$finite_day_of_intubation[is.infinite(mp$finite_day_of_intubation)] = NA
mp$day0_sample = factor(mp$day_of_intubation >= 0 & mp$day_of_intubation <=2)
mp$day0_sample[is.na(mp$day0_sample)] = FALSE
design(mp) = as.formula("~ Diagnosis")
metadata = as.data.frame(colData(mp))
```   
   
## Summary stats   
### Number of samples overall   
```{r}
table(mp$covid_confirmed, useNA = "always")
table(mp$pna_type, useNA = "always")
```   
   
### Number of patients   
```{r}
bulk_patients = colData(mp) %>% 
  as.data.frame() %>% 
  dplyr::select(study_id, covid_confirmed, pna_type) %>% 
  unique()
table(bulk_patients$covid_confirmed)
table(bulk_patients$pna_type)
```


### Number of serial samples   
```{r}
n_samples = table(metadata$patient_id)
serial_patients = names(n_samples[n_samples > 1])
length(serial_patients)
```   
   
### Distribution of days   
```{r}
ggplot(metadata, aes(x = day_of_intubation)) +
  geom_histogram(fill = "dodgerblue4")
```
   
## PCA   
### Calculate and organize   
```{r}
mp_vst = vst(mp, nsub = 3000, fitType = "local") #know from later that local is best
mp_counts = counts(estimateSizeFactors(mp), normalized = T)
pca_data = plotPCA_manual(mp_vst,
                          intgroup = c("covid_confirmed"),
                          pcs = 10)
pca_data$data = left_join(pca_data$data,
                     as.data.frame(colData(mp)),
                     by = c("name" = "sample", "covid_confirmed"))

#merge counts and pca data
gene_conv = as.data.frame(rowData(mp)) %>% 
  rownames_to_column(var = "ensembl_gene_id")
merge_counts = mp_counts %>% 
  t() %>%
  as.data.frame() %>% 
  rownames_to_column(var = "name")

#merge together for featureplot dataset
fp_data = left_join(pca_data$data, merge_counts, by = "name")
```

### How many PCs?   
```{r}
#scree plot   
fviz_eig(pca_data$pca, 
         barfill = "dodgerblue4", 
         xlab = "Principal Component",
         ylab = "Percent Variance Explained", 
         main = "",
         ncp = 50,
         ggtheme = theme_bw(),
         addlabels = T) #4-5 PCs will do it
```
   
### Examine loadings   
```{r}
loadings = pca_data$pca$rotation %>% 
  as.data.frame() %>% 
  rownames_to_column(var = "ensembl_gene_id") %>% 
  right_join(gene_conv, .)
```

   
### PC1: MoAM Character   
```{r}
ggplot(fp_data, aes(x = PC1, y = PC2, shape = covid_confirmed, color = percent_total_CD206_high)) +
  geom_point(size = 3)
fabp4_plot = ggplot(fp_data, aes(x = PC1, y = PC2, color = log2(ENSG00000170323))) +
  geom_point() +
  scale_color_viridis(name = "Log2(FABP4 Counts)")
spp1_plot = ggplot(fp_data, aes(x = PC1, y = PC2, color = log2(ENSG00000118785))) +
  geom_point() +
  scale_color_viridis(name = "Log2(SPP1 Counts)")
fabp4_plot + spp1_plot
```   
    
### PC2: Patterns of activation (also by milieu)   
```{r}
ggplot(fp_data, aes(x = PC1, y = PC2, shape = covid_confirmed, color = C_Reactive_Protein)) +
  geom_point(size = 3)
ggplot(fp_data, aes(x = PC1, y = PC2, shape = covid_confirmed, color = LDH)) +
  geom_point(size = 3)
ggplot(fp_data, aes(x = PC1, y = PC2, shape = covid_confirmed, color = FERRITIN)) +
  geom_point(size = 3)
inhba_plot = ggplot(fp_data, aes(x = PC1, y = PC2, color = log2(ENSG00000122641))) +
  geom_point() +
  scale_color_viridis(name = "Log2(INHBA)")
il1b_plot = ggplot(fp_data, aes(x = PC1, y = PC2, color = log2(ENSG00000125538))) +
  geom_point() +
  scale_color_viridis(name = "Log2(IL1b)")
```
After adding healthy controls this is longer true.   

   
### PC3: Male/female?   
```{r}
ggplot(fp_data, aes(x = PC2, y = PC3, color = gender)) +
  geom_point(size = 3)
xist_plot = ggplot(fp_data, aes(x = PC2, y = PC3, color = log2(ENSG00000229807))) +
  geom_point() +
  scale_color_viridis(name = "Log2(XIST)")
```   
Not really   
   
### Condition
```{r}
ggplot(fp_data, aes(x = PC1, y = PC2, color = Diagnosis)) +
  geom_point(size = 2) +
  stat_ellipse()
```   
   
### Day of intubation
```{r}
ggplot(fp_data, aes(x = PC1, y = PC2, color = log2(day_of_intubation))) +
  geom_point(size = 3)
```   
No clear separation.   
   
## Determine best dispersion estimates   
### Parametric   
```{r}
#note: DESeq is refitting far too many genes. Need to turn this off.
mp_des_parametric = DESeq(mp, 
                          fitType = "parametric",
                          parallel = T,
                          minReplicatesForReplace = Inf)
plotDispEsts(mp_des_parametric, cex = 0.1)
```   
   
This fit really isn't bad, but overestimates at lower expression (probably not a huge deal).   
    
### Local
```{r}
mp_des_local = DESeq(mp,
                     fitType = "local",
                     parallel = T,
                     minReplicatesForReplace=Inf)
plotDispEsts(mp_des_local, cex = 0.1)

mp_des = mp_des_local
rm(mp_des_local, mp_des_parametric)
```  
Still not perfect, but a lot better.   
   
```{r}
#used a few times later; safest to put here
#identify infected samples
non_human_genes = subset(gene_conv, !(grepl("^ENSG", ensembl_gene_id))) #human genes all have this prefix
non_human_genes$gene_name[non_human_genes$ensembl_gene_id == "gene-UJ99_s6gp1"] = "NA" #neuraminidase was misread
non_human_genes$virus = factor(ifelse(grepl("UJ99", non_human_genes$ensembl_gene_id),
                               yes = "Influenza A/California/07/2009",
                               no = "SARS-CoV-2"))
cov2_genes = subset(non_human_genes, grepl("SARS|GU280", ensembl_gene_id))
iav_genes = subset(non_human_genes, grepl("UJ99", ensembl_gene_id))
flu_counts = counts(mp_des, normalized = T)[iav_genes$ensembl_gene_id, ]
flu_detected = colnames(flu_counts[, colSums(flu_counts) > 0])
cov2_counts = counts(mp_des, normalized = T)[cov2_genes$ensembl_gene_id, ]
cov2_detected = colnames(cov2_counts[, colSums(cov2_counts) > 0])
mp_des$sars_cov2_detected = factor(mp_des$sample %in% cov2_detected)
mp_des$iav_h1n1_detected = factor(mp_des$sample %in% flu_detected)

## Replicating CoV2 samples
### overall
antisense_counts = cov2_counts["SARS_CoV_2_antisense_genome", ]
sense_counts = cov2_counts[2:nrow(cov2_counts), ]
replicating_cov2 = intersect(names(antisense_counts)[antisense_counts > 0],
                             colnames(sense_counts[, colSums(sense_counts) > 0]))
### just covid
percent_detected_covid = length(unique(subset(metadata, sample %in% cov2_detected & covid_confirmed == TRUE)$study_id)) / length(unique(subset(metadata, covid_confirmed == TRUE)$study_id)) * 100
percent_detected_covid
percent_replicating_covid = length(unique(subset(metadata, sample %in% replicating_cov2 & covid_confirmed == TRUE)$study_id)) / length(unique(subset(metadata, covid_confirmed == TRUE)$study_id)) * 100
percent_replicating_covid

percent_replicating_covid / percent_detected_covid * 100
```
      
## Sanity checks  
### SARS-CoV-2   
Does expression of viral genes correlate with covid diagnosis?   
```{r}
cov2_counts = lapply(cov2_genes$ensembl_gene_id, function(x){
  counts = plotCounts(mp,
                      gene = x,
                      intgroup = "Diagnosis",
                      returnData = T, 
                      pc = T)
  counts$gene = x
  return(counts)})
cov2_counts = bind_rows(cov2_counts)

ggplot(cov2_counts, aes(x = Diagnosis, y = count, fill = gene)) +
  geom_boxplot() +
  scale_y_log10()
```   
Now that we have all of the necessary metadata, this looks fantastic.  
   

      
### Do counts cluster by virus and diagnosis?   
```{r}
viral_counts = counts(mp_des, normalized = T) 
viral_counts = viral_counts[non_human_genes$ensembl_gene_id, ]
viral_counts = log10(viral_counts + 0.5)

virus_detection = vector(mode = "character", length = ncol(mp_des))
for(i in 1:ncol(mp_des))
{
  has_covid = mp_des$covid_confirmed[i] == TRUE
  has_iav_h1n1 = mp_des$INFLUENZA_A_H1_2009[i] == "Positive"
  has_other_coronavirus = mp_des$any_coronavirus_non_covid[i] == TRUE
  #recode NA as FALSE for safety
  if(is.na(has_iav_h1n1)) 
  {
    has_iav_h1n1 = FALSE
  }
  if(is.na(has_other_coronavirus)) 
  {
    has_other_coronavirus = FALSE
  }
  
  if(has_covid == TRUE && has_iav_h1n1 == TRUE && has_other_coronavirus == TRUE)
  {
    virus_detection[i] = "SARS-CoV-2 & Other Coronavirus & Influenza A/California/07/2009"
  } else if(has_covid == TRUE && has_iav_h1n1 == FALSE && has_other_coronavirus == FALSE)
  {
    virus_detection[i] = "SARS-CoV-2"
  } else if(has_covid == FALSE && has_iav_h1n1 == TRUE && has_other_coronavirus == FALSE)
  {
    virus_detection[i] = "Influenza A/California/07/2009"
  } else if(has_covid == FALSE && has_iav_h1n1 == FALSE && has_other_coronavirus == TRUE)
  {
     virus_detection[i] = "Other Coronavirus"
  } else if(has_covid == TRUE && has_iav_h1n1 == TRUE && has_other_coronavirus == FALSE)
  {
    virus_detection[i] = "SARS-CoV-2 & Influenza A/California/07/2009"
  } else if(has_covid == TRUE && has_iav_h1n1 == FALSE && has_other_coronavirus == TRUE)
  {
    virus_detection[i] = "SARS-CoV-2 & Other Coronavirus"
  } else if(has_covid == FALSE && has_iav_h1n1 == TRUE && has_other_coronavirus == TRUE)
  {
    virus_detection[i] = "Other Coronavirus & Influenza A/California/07/2009"
  } else
  {
    virus_detection[i] = NA
  }
}
mp_des$detected_viruses = factor(virus_detection)
  
diagnosis_annotation = as.data.frame(colData(mp_des)) %>% 
  dplyr::select(Diagnosis = detected_viruses)
gene_annotation = non_human_genes %>% 
  remove_rownames() %>% 
  column_to_rownames("ensembl_gene_id") %>% 
  dplyr::select(`Viral Origin` = virus)
gene_names = non_human_genes$gene_name
gene_names[gene_names == "SARS_CoV_2_antisense_genome"] = "Antisense"

viral_expression_heatmap = pheatmap(viral_counts,
        clustering_method = "ward.D2",
        show_colnames = F,
        color = inferno(100),
        annotation_col = diagnosis_annotation,
        annotation_row = gene_annotation,
        labels_row = gene_names,
        annotation_colors = list(Diagnosis = c("SARS-CoV-2" = fig2_pal[1], "Other Coronavirus" = fig2_pal[2],
                                                "Influenza A/California/07/2009" = fig2_pal[3], "Other Coronavirus & Influenza A/California/07/2009" = fig2_pal[4]),
                                  `Viral Origin` = c("SARS-CoV-2" = fig2_pal[1], "Influenza A/California/07/2009" = fig2_pal[3])),
        angle_col = 45, 
        annotation_names_row = F, 
        fontsize = 24)
```
   
### Patient 1174   
This patient apparently met criteria for COVID well before the first known cases in Chicago   
```{r}
cov2_counts = counts(mp_des, normalized = T)[cov2_genes$ensembl_gene_id, ]
samples_1174 = mp_des$study_id == "1174"
samples_1174[is.na(samples_1174)] = FALSE
tubes_1174 = mp_des$sample[samples_1174]
cov2_counts_1174 = cov2_counts[, tubes_1174]
cov2_counts_1174
```
No detection, at least in macrophages.   
   
### Do CoV2 reads decrease over time?   
Checking for viral clearance   
```{r}
covid_cases = mp_des[, mp_des$covid_confirmed == TRUE]
covid_counts = counts(covid_cases, normalized = T)
covid_counts = covid_counts[cov2_genes$ensembl_gene_id, ]

covid_counts = covid_counts %>% 
  as.data.frame() %>% 
  rownames_to_column(var = "gene") %>% 
  pivot_longer(cols = "340195046":"340239867",
               names_to = "sample",
               values_to = "Counts") %>% 
  left_join(., non_human_genes, by = c("gene" = "ensembl_gene_id")) %>% 
  dplyr::select(-c(gene, virus)) %>% 
  left_join(., as.data.frame(colData(mp_des)), by = "sample")

ggplot(covid_counts, aes(x = day_of_intubation, y = Counts)) +
  facet_wrap(~ gene_name, scales = "free_y") +
  geom_point() +
  geom_smooth(se = F)
```   
Unfortunately this is somewhat binarized. Better to treat it as such.   
   
```{r}
covid_counts_binarized = covid_counts %>% 
  group_by(sample) %>% 
  mutate(all_viral_counts = sum(Counts)) %>% 
  dplyr::select(sample, all_viral_counts, day_of_intubation) %>% 
  unique() %>% 
  mutate(virus_detected = all_viral_counts > 0)

time_cor = cor.test(covid_counts_binarized$all_viral_counts, covid_counts_binarized$day_of_intubation, method = "spearman")
time_cor_rho = round(time_cor$estimate, digits = 2)

sars_time_cor = ggplot(covid_counts_binarized, aes(x = day_of_intubation, y = all_viral_counts)) +
  geom_point() +
  scale_y_continuous(trans = "log10") +
  xlab("Day of Mechanical Ventilation") +
  ylab("SARS-CoV-2 Counts") +
  geom_smooth(se = F, color = fig2_pal[2]) +
  theme_bw(base_family = "Arial", base_size = 32) +
  theme(text = element_text(family = "Arial")) +
  annotate(x = 30, y = 5e4, label = paste("ρ = ", time_cor_rho), size = 10, geom = "text")
sars_time_cor
```

## Export for modeling core   
```{r eval=FALSE}
col_types = unlist(lapply(metadata, class))
list_cols = names(col_types[col_types == "AsIs"])
safe_metadata = metadata %>% 
  dplyr::select(-c(all_of(list_cols))) %>% 
  as.data.frame()
write.csv(safe_metadata, "/projects/b1038/Pulmonary/Workspace/COVID19_bal_ms/200709_bulk_metadata.csv")
write.csv(counts(mp_des, normalized = F), "/projects/b1038/Pulmonary/Workspace/COVID19_bal_ms/200709_bulk_counts_raw.csv")
write.csv(counts(mp_des, normalized = T), "/projects/b1038/Pulmonary/Workspace/COVID19_bal_ms/200709_bulk_counts_deseq2_normalized.csv")
```
      
## Which samples are worth sequencing further?   
### First batch   
```{r eval=FALSE}
good_samples = mp_des[, ((!is.na(mp_des$RIN) & mp_des$RIN >= 7) & 
                    mp_des$STAR_mqc_generalstats_star_uniquely_mapped_percent >= 30 &
                    mp_des$featureCounts_mqc_generalstats_featurecounts_percent_assigned >= 30) &
                    mp_des$RNA_concentration_pg_ul >= 125 |
                    (mp_des$iav_h1n1_detected == TRUE | mp_des$sars_cov2_detected == TRUE)]

selection_data = as.data.frame(colData(good_samples)) %>% 
  dplyr::select(sample, tc_pt_study_id, RIN, STAR_mqc_generalstats_star_uniquely_mapped_percent,
         featureCounts_mqc_generalstats_featurecounts_percent_assigned, iav_h1n1_detected, 
         sars_cov2_detected, covid_confirmed, batch, RNA_concentration_pg_ul, pna_type)
selection_data
```   
Keepers:   
- 340224808 (IAV)
- 304017553 (IAV + likely another coronavirus)
- 340239805 (Cov2)
- 340233896 (Cov2)
- 340239790 (Cov2)
- 340239789 (Cov2)
- 304012699 (Other -- likely another coronavirus)
- 300312389 (other)
- 304020362 (other)
   
```{r eval=FALSE}
keepers = selection_data %>% 
  dplyr::filter(sample %in% c(304020298, 340224808, 304017553, 340239805, 340233896,
                              340239790, 340239789, 304012699, 300312389, 304020362)) %>% 
  mutate(vol_for_250_pg = round(250 / RNA_concentration_pg_ul, digits = 3))
keepers$dilution = ifelse(keepers$vol_for_250_pg > 0.5,
                          yes = 1,
                          no = ifelse(keepers$vol_for_250_pg > 0.1,
                                      yes = 5,
                                      no = 20))
keepers$dilution_vol_for_250_pg = round(250 / (keepers$RNA_concentration_pg_ul / keepers$dilution), digits = 3)
write.csv(keepers, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200612_resequencing_samples.csv")
keepers
```   
   
# Signatures associated with SARS-CoV-2   
## Single genes   
### IL6   
```{r}
il6_counts = plotCounts(mp_des, 
           gene = "ENSG00000136244",
           normalized = T,
           intgroup = "covid_confirmed",
           returnData = T) %>% 
  rownames_to_column(var = "sample") %>% 
  left_join(., 
            metadata,
            by = c("sample", "covid_confirmed"))

cor.test(il6_counts$count, il6_counts$percent_total_CD206_high)
il6_plot = ggplot(il6_counts, aes(x = percent_total_CD206_high, y = count, color = covid_confirmed)) +
  geom_point(size = 3) +
  scale_y_log10() +
  ylab("IL6 Counts")
```   
Later confirmed NSD by group. Clearly correlates with MoAM character.        
   
### IL1b   
```{r}
il1b_counts = plotCounts(mp_des, 
           gene = "ENSG00000125538",
           normalized = T,
           intgroup = "covid_confirmed",
           returnData = T) %>% 
  rownames_to_column(var = "sample") %>% 
  left_join(., 
            metadata,
            by = c("sample", "covid_confirmed"))

cor.test(il1b_counts$count, il1b_counts$percent_total_CD206_high)
il1b_plot = ggplot(il1b_counts, aes(x = percent_total_CD206_high, y = count, color = covid_confirmed)) +
  geom_point(size = 3) +
  scale_y_log10() + 
  ylab("IL1b Counts")

il6_plot / il1b_plot
```
Later confirmed significantly downregulated, actually.   

## Basic DEA   
### Gene hits   
   
Versus healthy controls   
```{r}
cov2_vs_hc_results = as.data.frame(results(object = mp_des,
                                  contrast = c("Diagnosis", "COVID_19", "Healthy_Control"),
                                  alpha = 0.05,
                                  parallel = T))

cov2_vs_hc_results_ids = cov2_vs_hc_results %>% 
  rownames_to_column("ensembl_gene_id") %>% 
  left_join(., gene_conv) %>% 
  dplyr::filter(padj < 0.05 | !grepl("ENSG", ensembl_gene_id)) %>% 
  arrange(log2FoldChange)

write.csv(cov2_vs_hc_results_ids, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200717_standard_DEA_covid_over_healthycontrols.csv")

cov2_vs_hc_results_ids
```   
   
Upregulation of CoV-2 genes is gorgeous
   
Versus non-pna (sick) controls
```{r}
cov2_vs_npc_results = as.data.frame(results(object = mp_des,
                                  contrast = c("Diagnosis", "COVID_19", "Non_Pneumonia_Control"),
                                  alpha = 0.05,
                                  parallel = T))

cov2_vs_npc_results_ids = cov2_vs_npc_results %>% 
  rownames_to_column("ensembl_gene_id") %>% 
  left_join(., gene_conv) %>% 
  dplyr::filter(padj < 0.05 | !grepl("ENSG", ensembl_gene_id)) %>% 
  arrange(log2FoldChange)

write.csv(cov2_vs_npc_results_ids, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200717_standard_DEA_covid_over_nonpneumoniacontrols.csv")

cov2_vs_npc_results_ids
```
   
Versus other viral PNA
```{r}
cov2_vs_ovp_results = as.data.frame(results(object = mp_des,
                                  contrast = c("Diagnosis", "COVID_19", "Other_Viral_Pneumonia"),
                                  alpha = 0.05,
                                  parallel = T))

cov2_vs_ovp_results_ids = cov2_vs_ovp_results %>% 
  rownames_to_column("ensembl_gene_id") %>% 
  left_join(., gene_conv) %>% 
  dplyr::filter(padj < 0.05 | !grepl("ENSG", ensembl_gene_id)) %>% 
  arrange(log2FoldChange)

cov2_vs_ovp_results_ids

write.csv(cov2_vs_ovp_results_ids, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200717_standard_DEA_covid_over_otherviralpneumonia.csv")
```   
   
Versus other PNA
```{r}
cov2_vs_other_pna_results = as.data.frame(results(object = mp_des,
                                  contrast = c("Diagnosis", "COVID_19", "Other_Pneumonia"),
                                  alpha = 0.05,
                                  parallel = T))

cov2_vs_other_pna_results_ids = cov2_vs_other_pna_results %>% 
  rownames_to_column("ensembl_gene_id") %>% 
  left_join(., gene_conv) %>% 
  dplyr::filter(padj < 0.05 | !grepl("ENSG", ensembl_gene_id)) %>% 
  arrange(log2FoldChange)

write.csv(cov2_vs_other_pna_results_ids, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200717_standard_DEA_covid_over_otherpneumonia.csv")

cov2_vs_other_pna_results_ids
```  
   
### MA Plot   
```{r eval=FALSE}
pretty_MA_plot(cov2_vs_ovp_results, custom_annotation =  gene_conv)
```

   
### Heatmap   
   
How many clusters does it take?   
```{r eval=FALSE}
elbow = k_elbow(dge = mp_des, design = "~Diagnosis", 
        cores = 12, 
        minReps = Inf,
        random_seed = 12345,
        max_k = 25,
        qval_cutoff = 0.01)
elbow
```   
5 seems fair.   
    
```{r eval=FALSE}
intubation_pal = colorRampPalette(brewer.pal(n = 9, name = "Purples")[3:9])(max(mp_des$finite_day_of_intubation, na.rm = T) + 1)
intubation_pal[1:3] = rep("#000000",3) #highlight "D0"

cov2_kmeans_noclustering = k_means_figure(dge = mp_des,
                                 design = "~Diagnosis",
                                 k = 5,
                                 go_annotations = "org.Hs.eg.db",
                                 ensembl_db = "hsapiens_gene_ensembl",
                                 legend_factors = c("Diagnosis", "finite_day_of_intubation"),
                                 breaks = seq(-2, 2, length.out=101),
                                 cores = 12,
                             minReps = Inf,
                             return_genes = T,
                             display_go_terms = F,
                             return_go_terms = F,
                             cluster_columns = F,
                             show_rownames = F,
                             baseMeanCutoff = 20,
                             random_seed = 12345,
                             qval_cutoff = 0.05,
                             annotation_colors = list(Diagnosis = c("Healthy_Control" = fig2_pal[6],
                                                                    "Non_Pneumonia_Control" = fig2_pal[2],
                                                                    "COVID_19" = fig2_pal[1], 
                                                                    "Other_Viral_Pneumonia" = fig2_pal[3],
                                                                    "Other_Pneumonia" = fig2_pal[4]),
                                                      finite_day_of_intubation =  intubation_pal), 
                             custom_order = c(3, 5, 1, 2, 4), 
                             sortColumns = T,
                             column_sort_factors = c("Diagnosis", "finite_day_of_intubation"))

cov2_kmeans_clustered = k_means_figure(dge = mp_des,
                                 design = "~Diagnosis",
                                 k = 5,
                                 go_annotations = "org.Hs.eg.db",
                                 ensembl_db = "hsapiens_gene_ensembl",
                                 legend_factors = c("Diagnosis", "finite_day_of_intubation"),
                                 breaks = seq(-2, 2, length.out=101),
                                 cores = 12,
                             minReps = Inf,
                             return_genes = T,
                             display_go_terms = F,
                             return_go_terms = T,
                             cluster_columns = T,
                             show_rownames = F,
                             baseMeanCutoff = 20,
                             random_seed = 12345,
                             qval_cutoff = 0.05,
                             annotation_colors = list(Diagnosis = c("Healthy_Control" = fig2_pal[6],
                                                                    "Non_Pneumonia_Control" = fig2_pal[2],
                                                                    "COVID_19" = fig2_pal[1], 
                                                                    "Other_Viral_Pneumonia" = fig2_pal[3],
                                                                    "Other_Pneumonia" = fig2_pal[4]),
                                                      finite_day_of_intubation =  intubation_pal), 
                             custom_order = c(3, 5, 1, 2, 4))
for(i in 1:length(cov2_kmeans_clustered$GO))
{
  cov2_kmeans_clustered$GO[[i]]$cluster = i
}
cov2_kmeans_clustered$go_df = bind_rows(cov2_kmeans_clustered$GO) %>% 
  arrange(cluster, padj)
cov2_kmeans_clustered$expression = counts(mp_des, normalized = T) %>% 
  as.data.frame() %>% 
  rownames_to_column("ensembl_gene_id") %>% 
  dplyr::filter(ensembl_gene_id %in% cov2_kmeans_clustered$genes$gene) %>% 
  right_join(gene_conv, .)
saveRDS(cov2_kmeans_clustered, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200802_k_means_clustered.rds")
saveRDS(cov2_kmeans_noclustering, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_k_means_unclustered.rds")
write.csv(cov2_kmeans_clustered$genes, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_k_means_genes.csv")
write.csv(cov2_kmeans_clustered$go_df, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_k_means_GO.csv")
```   
   
This is actually very cool (and nicely foreshadows the later WGCNA results). C1: mostly COVID-related(!); highly enriched for type I interferon response (not production so much), as well as MHC-I. C2: mostly nonviral pneumonias. Pretty classic STAT3 inflammatory response. 
   
### D0 Heatmap   
```{r eval=FALSE}
d0_des = mp_des[, !is.na(mp_des$day_of_intubation) & mp_des$day_of_intubation <= 2 & mp_des$day_of_intubation >= 0]
# cov2_d0_kmeans_noclustering = k_means_figure(dge = d0_des, 
#                                  design = "~Diagnosis",
#                                  k = 3,
#                                  go_annotations = "org.Hs.eg.db",
#                                  ensembl_db = "hsapiens_gene_ensembl",
#                                  legend_factors = c("Diagnosis", "percent_total_CD206_high"),
#                                  breaks = seq(-3, 3, length.out=101),
#                                  cores = 12,
#                              minReps = Inf,
#                              return_genes = T,
#                              display_go_terms = F,
#                              return_go_terms = T,
#                              cluster_columns = F,
#                              label_fontsize = 2,
#                              customAnno = gene_conv,
#                              annoJoinCol = "ensembl_gene_id",
#                              sortColumns = T,
#                              colSortFactor = "Diagnosis",
#                              baseMeanCutoff = 20)

cov2_d0_kmeans_clustered = k_means_figure(dge = d0_des, 
                                 design = "~Diagnosis",
                                 k = 3,
                                 go_annotations = "org.Hs.eg.db",
                                 ensembl_db = "hsapiens_gene_ensembl",
                                 legend_factors = c("Diagnosis", "percent_total_CD206_high"),
                                 breaks = seq(-3, 3, length.out=101),
                                 cores = 12,
                             minReps = Inf,
                             return_genes = T,
                             display_go_terms = F,
                             return_go_terms = T,
                             cluster_columns = T,
                             label_fontsize = 2,
                             customAnno = gene_conv,
                             annoJoinCol = "ensembl_gene_id",
                             baseMeanCutoff = 20)
```
   
## Individual genes   
### CCL24   
This was an interesting COVID-specific hit. According to published data, it is a highly selective chemoattractant for T cells. Would expect that it should correlate with T cell abundance.   
   
```{r}
ccl24_counts = plotCounts(mp_des, 
                          gene = "ENSG00000106178",
                          intgroup = "Diagnosis",
                       normalized = T,
                       pc = T,
                       returnData = T)
ggplot(ccl24_counts, aes(x = Diagnosis, y = count, fill  = Diagnosis)) +
  geom_boxplot(width = 0.5, outlier.shape = NA) +
  geom_jitter(size = 0.1, width = 0.25) +
  scale_y_continuous(trans = "log10") +
  scale_fill_manual(name = "",
                    values = c("Healthy_Control" = fig2_pal[6],
                               "Non_Pneumonia_Control" = fig2_pal[2],
                               "COVID_19" = fig2_pal[1],
                               "Other_Viral_Pneumonia" = fig2_pal[3],
                               "Other_Pneumonia" = fig2_pal[4])) +
  scale_x_discrete(labels = c("Healthy_Control" = "Healthy\nControl",
                               "Non_Pneumonia_Control" = "Non-Pneumonia\nControl",
                               "COVID_19" = "COVID-19",
                               "Other_Viral_Pneumonia" = "Other Viral\nPneumonia",
                               "Other_Pneumonia" = "Other\nPneumonia")) +
  xlab("") +
  ylab("CCL24 Counts") +
  theme_bw() +
  theme(legend.position = "none", 
        text = element_text(size = 32, family = "Arial"),
        plot.title = element_text(hjust = 0.5))
```

   
```{r}
ccl24_cd4 = plotCounts(mp_des, 
                          gene = "ENSG00000106178",
                          intgroup = "percent_CD4_total",
                       normalized = T,
                       returnData = T) %>% 
  rownames_to_column("sample")
ccl24_cd8 = plotCounts(mp_des, 
                          gene = "ENSG00000106178",
                          intgroup = "percent_CD8_total",
                       normalized = T,
                       returnData = T) %>% 
  rownames_to_column("sample")
ccl24_tcells = left_join(ccl24_cd4, ccl24_cd8) %>% 
  pivot_longer(names_to = "flow_parameter", 
               values_to = "percent",
               cols = c(percent_CD8_total, percent_CD4_total))

ggplot(ccl24_tcells, aes(x = count, y = percent)) +
  geom_point() +
  geom_smooth(method = "lm") +
  facet_wrap(~flow_parameter) +
  scale_x_log10()
```
Pretty poor correlation   
   
## IL-6   
```{r}
il6_counts = plotCounts(mp_des, 
                          gene = "ENSG00000136244",
                          intgroup = "Diagnosis",
                       normalized = T,
                       pc = T,
                       returnData = T)
ggplot(il6_counts, aes(x = Diagnosis, y = count, fill  = Diagnosis)) +
  geom_boxplot(width = 0.5, outlier.shape = NA) +
  geom_jitter(size = 0.1, width = 0.25) +
  scale_y_continuous(trans = "log10") +
  scale_fill_manual(name = "",
                    values = c("Healthy_Control" = fig2_pal[6],
                               "Non_Pneumonia_Control" = fig2_pal[2],
                               "COVID_19" = fig2_pal[1],
                               "Other_Viral_Pneumonia" = fig2_pal[3],
                               "Other_Pneumonia" = fig2_pal[4])) +
  scale_x_discrete(labels = c("Healthy_Control" = "Healthy\nControl",
                               "Non_Pneumonia_Control" = "Non-Pneumonia\nControl",
                               "COVID_19" = "COVID-19",
                               "Other_Viral_Pneumonia" = "Other Viral\nPneumonia",
                               "Other_Pneumonia" = "Other\nPneumonia")) +
  xlab("") +
  ylab("IL-6 Counts") +
  theme_bw() +
  theme(legend.position = "none", 
        text = element_text(size = 32, family = "Arial"),
        plot.title = element_text(hjust = 0.5))
```

## Sasha's picks   
### All to start   
```{r}
sasha_genes = c("CCL20", "CCL24", "CCL3", "CCL8", "CCR2", "CCL2", "IL1A", "IL1B", "AREG", 
                "CXCR4", "TNFSF14", "CXCL3", "CXCL5", "CXCL1", "CXCL8", "ATF4", "CD164", 
                "MFGE8", "IL13RA1", "IL1RL1", "IL18R1", "VSIG4", "VEGFA", "DDIT4", "BAG3", 
                "DNAJB1", "HSP90AA6P", "HSPA1B", "HSPA1A")
sasha_genes = subset(gene_conv, gene_name %in% sasha_genes)

sasha_counts = mclapply(sasha_genes$ensembl_gene_id, function(x){
  counts = plotCounts(mp_des,
                      gene = x,
                      intgroup = "Diagnosis",
                      returnData = T,
                      normalized = T,
                      pc = T)
  counts$gene = x
  return(counts)})
sasha_counts = bind_rows(sasha_counts) %>% 
  left_join(., sasha_genes, by = c("gene" = "ensembl_gene_id"))

sasha_gene_plot = ggplot(sasha_counts, aes(x = Diagnosis, y = count, fill = Diagnosis)) +
  geom_boxplot(width = 0.5, outlier.shape = NA) +
  geom_jitter(size = 0.1, width = 0.25) +
  scale_y_continuous(trans = "log10") +
  scale_fill_manual(name = "",
                    values = c("Healthy_Control" = fig2_pal[6],
                               "Non_Pneumonia_Control" = fig2_pal[2],
                               "COVID_19" = fig2_pal[1],
                               "Other_Viral_Pneumonia" = fig2_pal[3],
                               "Other_Pneumonia" = fig2_pal[4])) +
  scale_x_discrete(labels = c("Healthy_Control" = "Healthy\nControl",
                               "Non_Pneumonia_Control" = "Non-Pneumonia\nControl",
                               "COVID_19" = "COVID-19",
                               "Other_Viral_Pneumonia" = "Other Viral\nPneumonia",
                               "Other_Pneumonia" = "Other\nPneumonia")) +
  xlab("") +
  ylab("Gene Counts") +
  theme_bw() +
  theme(legend.position = "none") +
  facet_wrap(~gene_name)
sasha_gene_plot
```   

   
## Clustering   
### All samples   
```{r}
detection_rate = function(x)
{
  return(sum(x > 0) / length(x)) 
}

all_counts = counts(mp_des, normalized = T)
#get 500 most variable genes with > 10% detection
rv = rowVars(all_counts)
prop_detected = apply(X = all_counts, MARGIN = 1, FUN = detection_rate)
selection_criteria = data.frame(sample = rownames(all_counts), var = rv, prop_detected = prop_detected) %>% 
  filter(prop_detected >= 0.1 & var > 0) %>% 
  arrange(desc(var))
top_genes = as.character(selection_criteria$sample[1:1000])
top_all_counts = all_counts[top_genes, ]

annotation = colData(mp_des) %>% 
  as.data.frame() %>% 
  dplyr::select(covid_confirmed, 
                sars_cov2_detected,
                sex,
                #day_of_intubation,
                infection_type,
                percent_neutrophils,
                percent_total_CD206_high)
gene_annotation = gene_conv %>% 
  dplyr::filter(ensembl_gene_id %in% rownames(top_all_counts)) %>% 
  column_to_rownames(var = "ensembl_gene_id")

pheatmap(top_all_counts,
         cluster_rows = T,
         cluster_cols = T,
         clustering_method = "ward.D2",
         annotation_col = annotation,
         labels_row = gene_annotation$gene_name,
         scale = "row",
         show_rownames = T,
         fontsize_row = 2,
         show_colnames = F,
         breaks = seq(-3, 3, length.out=101),
         color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(100),
         angle_col = 45)
```

### Day 0   
```{r}
day0_samples = which(mp$day_of_intubation <= 2)
day0 = mp_des[, day0_samples]
day0_counts = counts(day0, normalized = T)

#get 1000 most variable genes
rv = rowVars(day0_counts)
prop_detected = apply(X = day0_counts, MARGIN = 1, FUN = detection_rate)
selection_criteria = data.frame(sample = rownames(day0_counts), var = rv, prop_detected = prop_detected) %>% 
  filter(prop_detected >= 0.1 & var > 0) %>% 
  arrange(desc(var))
top_genes = as.character(selection_criteria$sample[1:1000])
top_day0_counts = day0_counts[top_genes, ]

annotation = colData(day0) %>% 
  as.data.frame() %>% 
  dplyr::select(covid_confirmed, 
                sars_cov2_detected,
                gender,
                infection_type,
                percent_neutrophils,
                percent_total_CD206_high)
gene_annotation = gene_conv %>% 
  dplyr::filter(ensembl_gene_id %in% rownames(top_day0_counts)) %>% 
  column_to_rownames(var = "ensembl_gene_id")

pheatmap(top_day0_counts,
         cluster_rows = T,
         cluster_cols = T,
         clustering_method = "ward.D2",
         annotation_col = annotation,
         labels_row = gene_annotation$gene_name,
         scale = "row",
         show_rownames = T,
         show_colnames = F,
         fontsize_row = 2,
         breaks = seq(-3, 3, length.out=101),
         color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(100),
         angle_col = 45)
```
   
## Comparing different pathogens   
### Individual pathogens   
```{r eval=FALSE}
pathogen_set = mp_des[, !is.na(mp$pathogen)]
pathogen_set$pathogen = factor(as.character(pathogen_set$pathogen)) #refactor
pathogen_kmeans = k_means_figure(dge = pathogen_set, 
                                 design = "~pathogen",
                                 k = 6,
                                 go_annotations = "org.Hs.eg.db",
                                 ensembl_db = "hsapiens_gene_ensembl",
                                 legend_factors = c("pathogen", "covid_confirmed"),
                                 breaks = seq(-3, 3, length.out=101),
                                 cores = 12)
```
Probably too granular   
   
### Broad groupings  
```{r eval=FALSE}
infection_type_kmeans = k_means_figure(dge = pathogen_set, 
                                 design = "~infection_type",
                                 k = 4,
                                 go_annotations = "org.Hs.eg.db",
                                 ensembl_db = "hsapiens_gene_ensembl",
                                 legend_factors = c("pathogen", "infection_type"),
                                 breaks = seq(-3, 3, length.out=101),
                                 cores = 12)
```

# Random   
## ACE2 counts   
```{r}
ace2_counts = plotCounts(mp_des, 
                         gene = "ENSG00000130234", 
                         intgroup = "covid_confirmed",
                         returnData = T,
                         normalized = T, 
                         pc = F)
ggplot(ace2_counts, aes(x = count)) +
  geom_histogram(fill = "dodgerblue4", ) +
  ylab("Number of Samples") +
  xlab("ACE2 Count")
```   
   
# Trajectories?   
## All samples   
We will use PCA as is recommended to make computation feasible.      
```{r}
#select genes with > 10% detection, mean counts > 5
all_counts = t(counts(mp_des, normalized = T)) #need size normalization
prop_detected = apply(X = all_counts, MARGIN = 2, FUN = detection_rate)
all_counts = all_counts[, prop_detected > 0.1]
mean_detection = colMeans(all_counts)
all_counts = all_counts[, mean_detection >= 5]

all_pca = prcomp(all_counts)
fviz_eig(all_pca, 
         barfill = "dodgerblue4", 
         xlab = "Principal Component",
         ylab = "Percent Variance Explained", 
         main = "",
         ncp = 50,
         ggtheme = theme_bw(),
         addlabels = T) #20 should capture almost everything

total_umap = umap(X = all_counts, 
                  pca = 20, 
                  pca_center = T, 
                  n_threads = 1, 
                  scale = "Z", 
                  min_dist = 0.2)
rownames(total_umap) = rownames(all_counts)
colnames(total_umap) = c("UMAP_1", "UMAP_2")
total_umap = as.data.frame(total_umap)
total_umap$sample = rownames(total_umap)
total_umap = left_join(total_umap, metadata, by = "sample") %>% 
  arrange(study_id, day_of_intubation) # so we can draw paths

ggplot(total_umap, aes(x = UMAP_1, y = UMAP_2, color = Diagnosis)) +
  geom_point() +
  stat_ellipse()

ggplot(total_umap, aes(x = UMAP_1, y = UMAP_2, shape = study_id, color = day_of_intubation)) +
  geom_path(arrow = grid::arrow()) +
  scale_color_viridis() +
  facet_wrap(~ Diagnosis)
```   
Patients actually separate out really well here. This might be worth including as a supplement. With that said, probably better to just use COVID patients for "trajectories". It looks like there may be some structure to the COVID data.      
   

## Only COVID   
```{r}
#remove non-detected genes
#note that PCA should only use genes detected in all samples (3081)
COVID_counts = t(counts(mp_des[, mp_des$Diagnosis == "COVID_19"], normalized = T)) #need size normalization
prop_detected = apply(X = COVID_counts, MARGIN = 2, FUN = detection_rate)
COVID_counts = COVID_counts[, prop_detected > 0.1]
mean_detection = colMeans(COVID_counts)
COVID_counts = COVID_counts[, mean_detection >= 5]

COVID_pca = prcomp(COVID_counts)
fviz_eig(COVID_pca, 
         barfill = "dodgerblue4", 
         xlab = "Principal Component",
         ylab = "Percent Variance Explained", 
         main = "",
         ncp = 50,
         ggtheme = theme_bw(),
         addlabels = T) #20 should capture almost everything

COVID_umap = umap(X = COVID_counts, 
                  pca = 20, 
                  pca_center = T, 
                  n_threads = 1, 
                  scale = "Z", 
                  min_dist = 0.4)
rownames(COVID_umap) = rownames(COVID_counts)
colnames(COVID_umap) = c("UMAP_1", "UMAP_2")
COVID_umap = as.data.frame(COVID_umap)
COVID_umap$sample = rownames(COVID_umap)
COVID_umap = left_join(COVID_umap, metadata, by = "sample") %>% 
  arrange(study_id, day_of_intubation) # so we can draw paths

ggplot(COVID_umap, aes(x = UMAP_1, y = UMAP_2, color = day_of_intubation)) +
  scale_color_viridis() +
  geom_point()
ggplot(COVID_umap, aes(x = UMAP_1, y = UMAP_2, shape = study_id, color = day_of_intubation)) +
  scale_color_viridis() +
  geom_path(arrow = grid::arrow())
```   
Not so much structure it seems.   

# WGCNA   
What gene modules are associated with covid? Which change substantially over time on ventilator?   
## Thesholding
```{r}
#setup
enableWGCNAThreads(nThreads = 12)
WGCNAnThreads()

# Choose a set of soft-thresholding powers
powers = c(1:20)

# Call the network topology analysis functions
sft = pickSoftThreshold(all_counts, powerVector = powers, verbose = 5, networkType = "signed")

# Plot the results:
sizeGrWindow(9, 5)
par(mfrow = c(1,2))
cex1 = 0.9

# Scale-free topology fit index as a function of the soft-thresholding power
plot(sft$fitIndices[,1], 
     -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
     xlab="Soft Threshold (power)",
     ylab="Scale Free Topology Model Fit,signed R^2",
     type="n",
     main = paste("Scale independence"))
text(sft$fitIndices[,1], 
     -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
     labels=powers,
     cex=cex1,
     col="red")

# this line corresponds to using an R^2 cut-off of h
abline(h=0.90,col="red")

# Mean connectivity as a function of the soft-thresholding power
plot(sft$fitIndices[,1], 
     sft$fitIndices[,5],
     xlab="Soft Threshold (power)",
     ylab="Mean Connectivity", 
     type="n",
     main = paste("Mean connectivity"))
text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red")
```   
7 looks like the intersection after adding healthy controls   
   
## Co-expression by similarity and adjacency   
```{r}
softPower = 7
adjacency = adjacency(all_counts, power = softPower, type = "signed")
```

## Topological overlap matrix   
```{r}
# Turn adjacency into topological overlap
TOM = TOMsimilarity(adjacency, TOMType = "signed")
dissTOM = 1-TOM
```

## Cluster by TOM distance   
```{r}
geneTree = hclust(as.dist(dissTOM), method = "average")

sizeGrWindow(12,9)
plot(geneTree, 
     xlab="", 
     sub="", 
     main = "Gene clustering on TOM-based dissimilarity",
     labels = FALSE, 
     hang = 0.04)
```   
   
## Create modules   
```{r}
minModuleSize = 30 # may need to adjust

# Make modules based on clusters
dynamicMods = cutreeDynamic(dendro = geneTree, 
                            distM = dissTOM,
                            deepSplit = 2,
                            pamRespectsDendro = FALSE,
                            minClusterSize = minModuleSize)
table(dynamicMods)
```   
   
### Label modules (yeah this is dumb -- rename later)   
```{r}
# Convert numeric labels into colors
dynamicColors = labels2colors(dynamicMods)
table(dynamicColors)

# Plot the dendrogram and colors underneath
sizeGrWindow(8,6)
plotDendroAndColors(geneTree, 
                    dynamicColors, 
                    "Dynamic Tree Cut",
                    dendroLabels = FALSE,
                    hang = 0.03,
                    addGuide = TRUE,
                    guideHang = 0.05,
                    main = "Gene dendrogram and module colors")
```   
Looks like we captured the structure pretty well.      
   
### Merge similar modules   
```{r}
# Calculate eigengenes
MEList = moduleEigengenes(all_counts, 
                          colors = dynamicColors)
MEs = MEList$eigengenes

# Calculate dissimilarity of module eigengenes
MEDiss = 1-cor(MEs)

# Cluster module eigengenes
METree = hclust(as.dist(MEDiss), 
                method = "average")

# Plot the result
sizeGrWindow(7, 6)
plot(METree, 
     main = "Clustering of module eigengenes",
     xlab = "",
     sub = "")

#Now cut at height of 0.5 to get correlation of 0.5  
#standard is 0.25, but we have some really similar modules that are not relevant
MEDissThres = 0.25

# Plot the cut line into the dendrogram
abline(h=MEDissThres, 
       col = "red")

# Call an automatic merging function
merge = mergeCloseModules(all_counts, 
                          dynamicColors, 
                          cutHeight = MEDissThres, 
                          verbose = 3)

# The merged module colors
mergedColors = merge$colors

# Eigengenes of the new merged modules:
mergedMEs = merge$newMEs

# Rename to moduleColors
moduleColors = mergedColors

# Construct numerical labels corresponding to the colors
colorOrder = c("grey", standardColors(50))
moduleLabels = match(moduleColors, colorOrder)-1
MEs = mergedMEs

#replot
sizeGrWindow(12, 9)
plotDendroAndColors(geneTree, 
                    cbind(dynamicColors, mergedColors),
                    c("Dynamic Tree Cut", "Merged dynamic"),
                    dendroLabels = FALSE, 
                    hang = 0.03,
                    addGuide = TRUE, 
                    guideHang = 0.05)
```   
No change   
   
## Relate back to traits   
### Without vent settings   
```{r}
# Define numbers of genes and samples
nGenes = ncol(all_counts)
nSamples = nrow(all_counts)
metadata = as.data.frame(colData(mp_des))
md_of_interest = metadata %>% 
  mutate(deceased = ifelse(binned_outcome == "Deceased",
                           yes = 1,
                           no = 0)) %>% 
  mutate(has_covid = ifelse(covid_confirmed == "TRUE",
                           yes = 1,
                           no = 0)) %>% 
  mutate(cov2_detected = ifelse(sars_cov2_detected == TRUE,
                                yes = 1,
                                no = 0)) %>% 
  mutate(has_pneumonia = ifelse(pna_type == "Non-Pneumonia Control",
                           yes = 0,
                           no = 1)) %>% 
  mutate(coinfection_detected = ifelse(any_nonviral == "TRUE",
                           yes = 1,
                           no = 0)) %>% 
  mutate(other_viral_pna = ifelse(pna_type == "Other Viral Pneumonia",
                           yes = 1,
                           no = 0)) %>% 
  mutate(nonviral_pna = ifelse(pna_type == "Other Pneumonia",
                           yes = 1,
                           no = 0)) %>% 
  mutate(is_healthy_control = ifelse(pna_type == "Healthy Control",
                           yes = 1,
                           no = 0)) %>% 
  dplyr::select(deceased, has_covid, cov2_detected, has_pneumonia,
                other_viral_pna, nonviral_pna, is_healthy_control,
                percent_neutrophils, percent_total_CD206_high,
                percent_CD4_total, percent_CD8_total, finite_day_of_intubation,
                C_Reactive_Protein, D_DIMER, PROCALCITONIN, mean_aps)
# Recalculate MEs with color labels
MEs0 = moduleEigengenes(all_counts, moduleColors)$eigengenes
MEs = orderMEs(MEs0)
moduleTraitCor = bicor(MEs, 
                     md_of_interest, 
                     use = "pairwise.complete.obs",
                     maxPOutliers = 0.05)
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, 
                                     nSamples) %>% 
  as.numeric() %>% 
  p.adjust(., method = "fdr") %>% 
  matrix(ncol = ncol(moduleTraitCor))
colnames(moduleTraitPvalue) = colnames(moduleTraitCor)
rownames(moduleTraitPvalue) = rownames(moduleTraitCor)
moduleTraitCor_hm = t(moduleTraitCor)
#just make significant or not
pvals_hm = t(moduleTraitPvalue) %>% 
  as.matrix()
pvals_hm[pvals_hm < 0.05] = ""
pvals_hm[pvals_hm != ""] = "NS"

labs = c("Deceased", "COVID-19", "SARS-CoV-2 Detected", "Any Pneumonia", "Other Viral Pneumonia", "Other Pneumonia",
         "Healthy Control", "% Neutrophils", "% Macrophages CD206-high", "% CD4 T", "% CD8 T",
         "Day of Intubation", "CRP", "D-Dimer", "Procalcitonin", "Mean APS")
module_cor_heatmap = pheatmap(mat = moduleTraitCor_hm,
                              labels_row = labs,
                              labels_col = paste("Module", c(1:ncol(MEs))),
                              display_numbers = pvals_hm,
                              cluster_rows = T,
                              cluster_cols = T,
                              clustering_method = "ward.D2",
                              color = colorRampPalette(rev(brewer.pal(n = 7, 
                                                          name = "RdBu")))(100),
                              angle_col = 315)
module_cor_heatmap = pheatmap(mat = moduleTraitCor_hm,
                              labels_row = labs,
                              labels_col = paste("Module", c(1:ncol(MEs))),
                              display_numbers = pvals_hm,
                              cluster_rows = T,
                              cluster_cols = T,
                              clustering_method = "ward.D2",
                              color = colorRampPalette(rev(brewer.pal(n = 7, 
                                                          name = "RdBu")))(100),
                              angle_col = 315, 
                              fontsize = 32)
```
Module 12 (turquoise) looks like the key MoAM cluster, 4 (blue) is key TRAM, and module 14 (purple) is likely our IFN cluster.

### With vent settings   
```{r}
# Define numbers of genes and samples
nGenes = ncol(all_counts)
nSamples = nrow(all_counts)

md_of_interest = metadata %>% 
  mutate(deceased = ifelse(binned_outcome == "Deceased",
                           yes = 1,
                           no = 0)) %>% 
  mutate(has_covid = ifelse(covid_confirmed == "TRUE",
                           yes = 1,
                           no = 0)) %>% 
  mutate(cov2_detected = ifelse(sars_cov2_detected == TRUE,
                                yes = 1,
                                no = 0)) %>% 
  mutate(has_pneumonia = ifelse(pna_type == "Non-Pneumonia Control",
                           yes = 0,
                           no = 1)) %>% 
  mutate(coinfection_detected = ifelse(any_nonviral == "TRUE",
                           yes = 1,
                           no = 0)) %>% 
  mutate(other_viral_pna = ifelse(pna_type == "Other Viral Pneumonia",
                           yes = 1,
                           no = 0)) %>% 
  mutate(nonviral_pna = ifelse(pna_type == "Other Pneumonia",
                           yes = 1,
                           no = 0)) %>% 
  mutate(is_healthy_control = ifelse(pna_type == "Healthy Control",
                           yes = 1,
                           no = 0)) %>% 
  dplyr::select(deceased, has_covid, cov2_detected, has_pneumonia,
                other_viral_pna, nonviral_pna, is_healthy_control,
                percent_neutrophils, percent_total_CD206_high,
                percent_CD4_total, percent_CD8_total, finite_day_of_intubation,
                C_Reactive_Protein, D_DIMER, PROCALCITONIN, mean_aps,
                Static_Compliance, PF_ratio)

# Recalculate MEs with color labels
MEs0 = moduleEigengenes(all_counts, moduleColors)$eigengenes
MEs = orderMEs(MEs0)
moduleTraitCor = bicor(MEs, 
                     md_of_interest, 
                     use = "pairwise.complete.obs",
                     maxPOutliers = 0.05)

moduleTraitPvalue = corPvalueStudent(moduleTraitCor, 
                                     nSamples) %>% 
  as.numeric() %>% 
  p.adjust(., method = "fdr") %>% 
  matrix(ncol = ncol(moduleTraitCor))
colnames(moduleTraitPvalue) = colnames(moduleTraitCor)
rownames(moduleTraitPvalue) = rownames(moduleTraitCor)

moduleTraitCor_hm = t(moduleTraitCor)
#just make significant or not
pvals_hm = t(moduleTraitPvalue) %>% 
  as.matrix()
pvals_hm[pvals_hm < 0.05] = ""
pvals_hm[pvals_hm != ""] = "NS"


labs = c("Deceased", "COVID-19", "SARS-CoV-2 Detected", "Any Pneumonia", "Other Viral Pneumonia", "Other Pneumonia",
         "Healthy Control", "% Neutrophils", "% Macrophages CD206-high", "% CD4 T", "% CD8 T",
         "Day of Intubation", "CRP", "D-Dimer", "Procalcitonin", "Mean APS",
         "Static Compliance", "P/F Ratio")
module_cor_heatmap_vent_mini = pheatmap(mat = moduleTraitCor_hm,
                              labels_row = labs,
                              labels_col = paste("Module", c(1:ncol(MEs))),
                              display_numbers = pvals_hm,
                              cluster_rows = T,
                              cluster_cols = T,
                              clustering_method = "ward.D2",
                              color = colorRampPalette(rev(brewer.pal(n = 7, 
                                                          name = "RdBu")))(100),
                              angle_col = 315)
module_cor_heatmap_vent = pheatmap(mat = moduleTraitCor_hm,
                              labels_row = labs,
                              labels_col = paste("Module", c(1:ncol(MEs))),
                              display_numbers = pvals_hm,
                              cluster_rows = T,
                              cluster_cols = T,
                              clustering_method = "ward.D2",
                              color = colorRampPalette(rev(brewer.pal(n = 7, 
                                                          name = "RdBu")))(100),
                              angle_col = 315, 
                              fontsize = 24)
saveRDS(module_cor_heatmap_vent, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200803_WGCNA_cor_heatmap.rds")
```   
Module 13 (salmon) may also be interesting. Correlates with COVID, CRP, lymphocytes, and vent params!    
   
### Sanity check: are the CD206 modules TRAM/MoAM markers?   
```{r}
module_assignments = data.frame(ensembl_gene_id = colnames(all_counts),
                                module = factor(mergedColors)) %>% 
  left_join(., gene_conv)
write.csv(module_assignments,
          "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_WGCNA_module_assignments.csv")

cd206_correlated = subset(module_assignments, module == "blue")
cd206_anticorrelated = subset(module_assignments, module == "turquoise")
cd206_correlated
cd206_anticorrelated
```
Pretty ideal, yeah   
   
   
### COVID-associated   
Purple contains all of the CoV-2 genes!   
```{r}
covid_correlated_cd206_uncorrelated = subset(module_assignments, module == "purple")
covid_correlated_cd206_uncorrelated
write.csv(covid_correlated_cd206_uncorrelated,
          "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_purple_module_14_genes.csv")
```
Pretty classic interferon response. Look at the GO.   
   
```{r}
#from k_means_figure
complete_counts = counts(mp_des, normalized = T)
universe = rownames(complete_counts[rowSums(complete_counts) > 0, ])
fisherTest = new("classicCount", testStatistic = GOFisherTest, name = "Fisher test")

cluster_genes = covid_correlated_cd206_uncorrelated$ensembl_gene_id
selection = as.numeric(universe %in% cluster_genes)
names(selection) = universe
go_data = new("topGOdata", 
              ontology = "BP", 
              allGenes = selection,
              geneSel = function(x){
                return(x == 1)},
              annot = annFUN.org, 
              mapping = "org.Hs.eg.db", 
              ID = "ensembl")
  
#run Fisher test
test_results = getSigGroups(go_data, fisherTest)
module14_score = as.data.frame(score(test_results))
colnames(module14_score) = "pval"
module14_score = rownames_to_column(module14_score, var = "go_id")
  
#adjust p-values and take significant
module14_score$padj = p.adjust(module14_score$pval, method = "fdr")
module14_score = subset(module14_score, padj < 0.05)
module14_score$description = NA
for(i in 1:nrow(module14_score))
{
  module14_score$description[i] = GOTERM[[module14_score$go_id[i]]]@Term
}

#add descriptions
module14_score$full_go = paste(module14_score$go_id, module14_score$description)
write.csv(module14_score,
          "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_purple_module_14_GO.csv")
module14_score
```
Pretty much all Type I interferon related. Most of the paper is here.   
   
### Module 13   
This module has IRF1 and the MHC-I genes   
```{r}
module13 = subset(module_assignments, module == "salmon")
module13
write.csv(module13,
          "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_salmon_module_13_genes.csv")
```
   
```{r}
#from k_means_figure
cluster_genes = module13$ensembl_gene_id
selection = as.numeric(universe %in% cluster_genes)
names(selection) = universe
go_data = new("topGOdata", 
              ontology = "BP", 
              allGenes = selection,
              geneSel = function(x){
                return(x == 1)},
              annot = annFUN.org, 
              mapping = "org.Hs.eg.db", 
              ID = "ensembl")
  
#run Fisher test
test_results = getSigGroups(go_data, fisherTest)
module13_score = as.data.frame(score(test_results))
colnames(module13_score) = "pval"
module13_score = rownames_to_column(module13_score, var = "go_id")
  
#adjust p-values and take significant
module13_score$padj = p.adjust(module13_score$pval, method = "fdr")
module13_score = subset(module13_score, padj < 0.05)
# module13_score$description = NA
# for(i in 1:nrow(module13_score))
# {
#   module13_score$description[i] = GOTERM[[module13_score$go_id[i]]]@Term
# }
# 
# #add descriptions
# module13_score$full_go = paste(module13_score$go_id, module13_score$description)
# write.csv(module13_score,
#           "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_salmon_module_13_GO.csv")
# module13_score
```
No significant hits.   
   
## Plot interesting modules   
### Purple module (14) and diagnosis, outcome   
```{r}
total_umap = MEList$averageExpr %>% 
  rownames_to_column("sample") %>% 
  left_join(total_umap, .)
covid_umap = total_umap %>% 
  dplyr::filter(covid_confirmed == TRUE)

ggplot(total_umap, aes(x = UMAP_1, y = UMAP_2, size = AEpurple, color = Diagnosis)) +
  geom_point()
module14_umap = ggplot(total_umap, aes(x = UMAP_1, y = UMAP_2, size = AEpurple, color = pna_type, shape = binned_outcome)) +
  geom_point() +
   theme_bw() +
  theme(text = element_text(size = 32, family = "Arial"),
        plot.title = element_text(hjust = 0.5)) +
  scale_color_manual(name = "",
                    values = c("Healthy_Control" = fig2_pal[6],
                               "Non-Pneumonia Control" = fig2_pal[2],
                               "COVID-19" = fig2_pal[1],
                               "Other Viral Pneumonia" = fig2_pal[3],
                               "Other Pneumonia" = fig2_pal[4]),
                    na.value = "black") +
  scale_shape_manual(name = "",
                    values = c("Deceased" = 13,
                               "Discharged" = 19,
                               "Inpatient Facility" = 18,
                               "Other" = 18),
                               na.value = 1) +
  scale_size_continuous(name = "Module 14 Expression", 
                        range = c(1, 10)) +
  xlab("UMAP 1") +
  ylab("UMAP 2")
module14_umap 
```   
   
### CCL24      
```{r}
ccl24_counts = rownames_to_column(ccl24_counts, var = "sample")
ccl24_counts = ccl24_counts[, 1:2]
colnames(ccl24_counts)[2] = "CCL24"
total_umap = left_join(total_umap, ccl24_counts)
ccl24_umap = ggplot(total_umap, aes(x = UMAP_1, y = UMAP_2, size = CCL24, color = pna_type, shape = binned_outcome)) +
  geom_point() +
  theme_bw() +
  theme(text = element_text(size = 16, family = "Arial"),
        legend.text = element_text(size = 16, family = "Arial"),
        legend.title = element_text(size = 16, family = "Arial")) +
  scale_color_manual(name = "",
                    values = c("Healthy_Control" = fig2_pal[6],
                               "Non-Pneumonia Control" = fig2_pal[2],
                               "COVID-19" = fig2_pal[1],
                               "Other Viral Pneumonia" = fig2_pal[3],
                               "Other Pneumonia" = fig2_pal[4]),
                    na.value = "black") +
  scale_shape_manual(name = "",
                    values = c("Deceased" = 13,
                               "Discharged" = 19,
                               "Inpatient Facility" = 18,
                               "Other" = 18),
                               na.value = 1) +
scale_size_continuous(name = "CCL24 Counts", trans = "log10", range = c(1, 10)) +
  xlab("UMAP 1") +
  ylab("UMAP 2")
ccl24_umap 
```
This is really cool! This isolates most the the IFN-non-responsive COVID patients. Maybe this drives heterogeneity in celltype composition in the lung?   
   
```{r}
tcell_umap = ggplot(total_umap, aes(x = UMAP_1, y = UMAP_2, size = percent_CD3_total, color = pna_type, shape = binned_outcome)) +
  geom_point() +
  theme_bw() +
  theme(text = element_text(size = 16, family = "Arial"),
        legend.text = element_text(size = 16, family = "Arial"),
        legend.title = element_text(size = 16, family = "Arial")) +
  scale_color_manual(name = "",
                    values = c("Healthy_Control" = fig2_pal[6],
                               "Non-Pneumonia Control" = fig2_pal[2],
                               "COVID-19" = fig2_pal[1],
                               "Other Viral Pneumonia" = fig2_pal[3],
                               "Other Pneumonia" = fig2_pal[4]),
                    na.value = "black") +
  scale_shape_manual(name = "",
                    values = c("Deceased" = 13,
                               "Discharged" = 19,
                               "Inpatient Facility" = 18,
                               "Other" = 18),
                               na.value = 1) +
scale_size_continuous(name = "Percent Lymphocytes", range = c(1, 10)) +
  xlab("UMAP 1") +
  ylab("UMAP 2")

tcell_umap 
```


   
### Brown/turquoise modules and day of intubation in COVID patients   
TRAM content   
```{r}
ggplot(total_umap, aes(x = day_of_intubation, y = AEblue)) +
  geom_point() +
  geom_smooth()

ggplot(total_umap, aes(x = day_of_intubation, y = AEturquoise)) +
  geom_point() +
  geom_smooth()
```
Not much of a correlation...maybe a flat line, as I guess would be expected with near-constant recruitment. 
   
## Effect of ORF8 expression   
### Isolate counts   
```{r}
orf8 = cov2_genes$ensembl_gene_id[which(cov2_genes$gene_name == "ORF8")]
orf8_counts = plotCounts(mp_des, 
                         gene = orf8, 
                         intgroup = "pna_type",
                         returnData = T,
                         normalized = T, 
                         pc = T)
orf8_samples = rownames(subset(orf8_counts, count >0))

ggplot(orf8_counts, aes(x = pna_type, y = count)) +
  geom_boxplot() +
  geom_jitter(aes(color = count > 0)) +
  scale_y_log10() +
  ylab("ORF8 Counts") +
  xlab("") 
```   
   
Not a ton of detection but very clean.   
   
### Association with interferon responses   
```{r}
mp_des$orf8_detected = mp_des$sample %in% orf8_samples
total_umap$orf8_detected = total_umap$sample %in% orf8_samples
ggplot(total_umap, aes(x = UMAP_1, y = UMAP_2, size = AEpurple, color = Diagnosis, shape = orf8_detected)) +
  geom_point()
```  
Pretty clear spatial correlation...but in the opposite direction?   
   
```{r}
ggplot(total_umap, aes(x = orf8_detected, y = AEpurple)) +
  geom_boxplot() +
  geom_jitter(aes(color = Diagnosis))

high_cov2 = colnames(cov2_counts)[colSums(cov2_counts) > 50]
total_umap$cov2_detected = total_umap$sample %in% high_cov2
ggplot(total_umap, aes(x = cov2_detected, y = AEpurple)) +
  geom_boxplot() +
  geom_jitter(aes(color = Diagnosis))
```
This really just correlates with overall viral load (which correlates, in turn, with ORF8 levels). No support really for the ORF8 inhibition of the IFN response, at least at this stage.   
   
# Interferon response over time   
## WGCNA Module purple   
### Average expression   
```{r}
m14_cor = cor.test(covid_umap$AEpurple, covid_umap$finite_day_of_intubation)
module_14_corplot = ggplot(covid_umap, aes(x = finite_day_of_intubation, y = AEpurple)) +
  geom_point() +
  annotate(geom = "text", x = 30, y = 2, label = paste0("R = ", round(m14_cor$estimate, digits = 2)), size = 6) +
  geom_smooth(method = "lm", color = fig2_pal[2]) +
  theme_bw() +
  xlab("Day of Mechanical Ventilation") +
  ylab("Average Module 14 Expression") +
  theme(legend.position = "none", 
        text = element_text(size = 32, family = "Arial"),
        plot.title = element_text(hjust = 0.5))
module_14_corplot
```
   
## GO Categories   
### Pull from biomart   
```{r}
human_mart = useEnsembl("ensembl", dataset = "hsapiens_gene_ensembl", GRCh = 37)

#now pull down genes based on gene ontology (GO) definitions
typeI_IFN_response = getBM(attributes = c("ensembl_gene_id", "external_gene_name"),
                           filters = "go",
                           values = "GO:0060337",
                           mart = human_mart)

IFNg_response = getBM(attributes = c("ensembl_gene_id", "external_gene_name"),
                           filters = "go",
                           values = "GO:0060333",
                           mart = human_mart)
```   
   
### Assign mean expression   
```{r}
unfiltered_counts = counts(mp_des, normalized = T)
typeI_IFN_response = subset(typeI_IFN_response, ensembl_gene_id %in% rownames(unfiltered_counts))
IFNg_response = subset(IFNg_response, ensembl_gene_id %in% rownames(unfiltered_counts))
type1_counts = unfiltered_counts[typeI_IFN_response$ensembl_gene_id, mp_des$covid_confirmed == TRUE]
type1_counts = as.data.frame(colMeans(type1_counts)) %>% 
  rownames_to_column("sample")
colnames(type1_counts)[2] = "typeI_IFN_response"
type2_counts = unfiltered_counts[IFNg_response$ensembl_gene_id, mp_des$covid_confirmed == TRUE]
type2_counts = as.data.frame(colMeans(type2_counts)) %>% 
  rownames_to_column("sample")
colnames(type2_counts)[2] = "IFNg_response"
ifn_counts = unfiltered_counts[intersect(IFNg_response$ensembl_gene_id, typeI_IFN_response$ensembl_gene_id),
                               mp_des$covid_confirmed == TRUE]
ifn_counts = as.data.frame(colMeans(ifn_counts)) %>% 
  rownames_to_column("sample")
colnames(ifn_counts)[2] = "general_IFN_response"

covid_umap = covid_umap %>% 
  left_join(., type1_counts) %>% 
  left_join(., type2_counts) %>% 
  left_join(., ifn_counts)
```
   
### Correlations   
```{r}
typei_cor = cor.test(covid_umap$typeI_IFN_response, covid_umap$finite_day_of_intubation)
typeii_cor = cor.test(covid_umap$IFNg_response, covid_umap$finite_day_of_intubation)
general_cor = cor.test(covid_umap$general_IFN_response, covid_umap$finite_day_of_intubation)
```
   
### Plot   
```{r}
#GO:0060337
typei_corplot = ggplot(covid_umap, aes(x = finite_day_of_intubation, y = typeI_IFN_response)) +
  geom_point() + 
  geom_smooth(method = "lm", color = fig2_pal[2]) +
  annotate(geom = "text", x = 30, y = 1750, label = paste0("R = ", round(typei_cor$estimate, digits = 2)), size = 6) +
  theme_bw() +
  theme(legend.position = "none", 
        text = element_text(size = 32, family = "Arial"),
        plot.title = element_text(hjust = 0.5)) +
  ylab("Average Type I IFN Pathway Gene Expression") +
  xlab("Day of Mechanical Ventilation")
typei_corplot 

#GO:0060333
typeii_corplot = ggplot(covid_umap, aes(x = finite_day_of_intubation, y = IFNg_response)) +
  geom_point() + 
  geom_smooth(method = "lm", color = fig2_pal[2]) +
  annotate(geom = "text", x = 30, y = 3000, label = paste0("R = ", round(typeii_cor$estimate, digits = 2)), size = 6) +
  theme_bw() +
  theme(legend.position = "none", 
        text = element_text(size = 32, family = "Arial"),
        plot.title = element_text(hjust = 0.5)) +
  ylab("Average IFNg Pathway Gene Expression") +
  xlab("Day of Mechanical Ventilation")
typeii_corplot

ggplot(covid_umap, aes(x = finite_day_of_intubation, y = general_IFN_response)) +
  geom_point() + 
  geom_smooth(method = "lm", color = fig2_pal[2]) +
  theme_bw() +
  theme(legend.position = "none", 
        text = element_text(size = 32, family = "Arial"),
        plot.title = element_text(hjust = 0.5)) +
  ylab("Average IFN response Expression") +
  xlab("Day of Mechanical Ventilation")
```   


   
# Second batch of pico optimization   
Let's focus on interferon-hi vs interferon-low samples   
### Identify interesting   
```{r}
last_batch = c(300312389, 304012699, 304017553, 304020298, 304020362,
               340224808, 340233896, 340239789, 340239790, 340239805)
good_samples = mp_des[, ((!is.na(mp_des$RIN) & mp_des$RIN >= 7) & 
                    mp_des$STAR_mqc_generalstats_star_uniquely_mapped_percent >= 30 &
                    mp_des$featureCounts_mqc_generalstats_featurecounts_percent_assigned >= 30) &
                    mp_des$RNA_concentration_pg_ul >= 125]
remaining_interesting = setdiff(good_samples$sample, last_batch)
interesting_metadata = subset(total_umap, sample %in% remaining_interesting)
table(interesting_metadata$pna_type)
ggplot(interesting_metadata, aes(x = pna_type, y = AEpurple)) +
  geom_boxplot()
selection = interesting_metadata %>% 
  filter(pna_type != "Other Pneumonia" | sample == 340235110) %>% 
  dplyr::select(sample, tube_loc_macrophRNA_boxID, RNA_concentration_pg_ul) %>% 
  mutate(dilution = ifelse((250 / RNA_concentration_pg_ul) < 0.4,
                           yes = round(1 / (250 / RNA_concentration_pg_ul) / 5) * 5,
                           no = 1)) %>% 
  mutate(vol_for_250_pg = round(250 / RNA_concentration_pg_ul * dilution, digits = 2))
write.csv(selection, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200713_pico_opt_batch2.csv")
selection
```
   
# Conclusions   
## Output data for posterity   
```{r eval=FALSE}
final_counts_raw = counts(mp_des, normalized = F)

#clean metadata for safe sharing
final_md_safe = colData(mp_des) %>% 
  as.data.frame() %>% 
  dplyr::select(sample, age, sex, diagnosis = pna_type, 
                day_of_ventilation = finite_day_of_intubation, study_id)
#convert script IDs to new identifiers
id_conv = data.frame(script_id = sample(unique(final_md_safe$study_id))) %>% #shuffle IDs first
  mutate(anonymized_patient_id = sprintf("%04d", 1:nrow(id_conv)))
#add back to md
final_md_safe = left_join(final_md_safe, id_conv, by = c("study_id" = "script_id")) %>% 
  dplyr::select(-study_id)

write.csv(final_counts_raw, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/GEO_output/200724_raw_counts_geo.csv")
write.csv(final_md_safe, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/GEO_output/200724_deidentified_metadata_geo.csv")
```
   
## Export supplemental files   
### DEA
```{r eval=FALSE}
all_comparisons = combn(levels(mp_des$Diagnosis), 2, simplify = F)
names(all_comparisons) = vapply(all_comparisons, function(x){
  return(paste0(x[1], " vs ", x[2]))}, "char")
dea_hits = lapply(all_comparisons, function(x){
  hits = as.data.frame(results(object = mp_des,
                                  contrast = c("Diagnosis", x[1], x[2]),
                                  alpha = 0.05,
                                  parallel = T)) %>% 
    rownames_to_column("ensembl_gene_id") %>% 
    left_join(., gene_conv) %>% 
    arrange(padj)
  return(hits)})
names(dea_hits) = names(all_comparisons)
saveRDS(dea_hits, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/GEO_output/200728_bulk_dea_complete.rds")
```
   
### Export as single excel file   
```{r eval=FALSE}
output_genes = cov2_kmeans_clustered$genes %>% 
  dplyr::select(ensembl_gene_id = gene, cluster, external_gene_name) %>% 
  arrange(cluster)
names(dea_hits) = gsub("Healthy_Control", "HC", names(dea_hits))
names(dea_hits) = gsub("Other_Viral_Pneumonia", "OVP", names(dea_hits))
names(dea_hits) = gsub("Other_Pneumonia", "OP", names(dea_hits))
names(dea_hits) = gsub("Non_Pneumonia_Control", "NPC", names(dea_hits))
names(dea_hits) = gsub("COVID_19", "COVID-19", names(dea_hits))
all_sheets = c(list("k-means genes" = output_genes,
                    "k-means GO enrichment" = cov2_kmeans_clustered$go_df),
               dea_hits)
write.xlsx(x = all_sheets, 
           file = "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200728_bulk_supplmental_data.xlsx")
```
   
## Assemble figures   
### Figure 3   
```{r}
dea_hits = readRDS("/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/GEO_output/200728_bulk_dea_complete.rds")
cov2_kmeans_clustered = readRDS("/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_k_means_clustered.rds")
fig3_heatmap = cov2_kmeans_clustered$plot
fig3_heatmap_gg = as.ggplot(fig3_heatmap)
viral_expression_heatmap_gg = as.ggplot(viral_expression_heatmap)
module_cor_heatmap_vent_gg = as.ggplot(module_cor_heatmap_vent)

layout = "AABB\nAACC\nDEFG"
bulk_plot_fig3 = fig3_heatmap_gg +
  viral_expression_heatmap_gg +
  module_cor_heatmap_vent_gg +
  module14_umap +
  module_14_corplot +
  typei_corplot +
  typeii_corplot +
    plot_layout(design = layout) +
    plot_annotation(tag_levels = 'a', 
                  theme = theme(text = element_text(size = 120, family = "Arial"),
                                legend.text=element_text(size=120)))

bulk_plot_fig3
```
   
### Figure S3  
Marker genes   
```{r}
fig3_genes = c("IL6", "CCL24", "CCL8", "CCL2", "CCL7", 
               "RNASE1", "CCR1", "MAFB", "CSF2RA", "BAG3", 
               "HIF1", "LAMP3", "CCL20", "IL1A", "IL1B",
               "CD164", "MFGE8", "FCGR2A", "IL13RA1", "FABP4",
               "MSR1", "AXL", "C1QA", "ALOX5", "MARCO")
gene_comps = lapply(names(dea_hits), function(x)
{
  sub = subset(dea_hits[[x]], gene_name %in% fig3_genes) %>% 
    dplyr::filter(padj < 0.05)
  if(nrow(sub) == 0)
  {
    return(NULL)
  }
  comp = unlist(strsplit(x, split = " vs "))
  sub$numerator = comp[1]
  sub$denominator = comp[2]
  return(sub)
})
gene_comps = bind_rows(gene_comps) 

fig3_genes = fig3_genes[fig3_genes %in% c("IL6", unique(gene_comps$gene_name))] #keep only genes with sig diffs and IL6
fig3_genes = subset(gene_conv, gene_name %in% fig3_genes)
s3_counts = mclapply(fig3_genes$ensembl_gene_id, function(x){
  counts = plotCounts(mp_des,
                      gene = x,
                      intgroup = "Diagnosis",
                      returnData = T,
                      normalized = T,
                      pc = T)
  counts$gene = x
  return(counts)})
s3_counts = bind_rows(s3_counts) %>% 
  left_join(., fig3_genes, by = c("gene" = "ensembl_gene_id"))

#add y positions automatically
gene_comps = mutate(gene_comps, label = format(padj, digits = 2, scientific = T)) %>% 
  arrange(gene_name) %>% 
  group_by(gene_name, .drop = T) %>% 
  mutate(y = max(dplyr::filter(s3_counts, gene == unique(ensembl_gene_id))$count) * 10 ^ (1 + 0.5 * c(1:n()))) %>% 
  mutate(y_log10 = (log10(y) - 1.3))

marker_gene_plot = ggplot(s3_counts, aes(x = Diagnosis, y = count, fill = Diagnosis)) +
  geom_boxplot(width = 0.5, outlier.shape = NA) +
  geom_jitter(width = 0.25) +
  scale_y_continuous(trans = "log10") +
  scale_fill_manual(name = "",
                    values = c("Healthy_Control" = fig2_pal[6],
                               "Non_Pneumonia_Control" = fig2_pal[2],
                               "COVID_19" = fig2_pal[1],
                               "Other_Viral_Pneumonia" = fig2_pal[3],
                               "Other_Pneumonia" = fig2_pal[4])) +
  scale_x_discrete(labels = c("Healthy_Control" = "Healthy\nControl",
                               "Non_Pneumonia_Control" = "Non-Pneumonia\nControl",
                               "COVID_19" = "COVID-19",
                               "Other_Viral_Pneumonia" = "Other Viral\nPneumonia",
                               "Other_Pneumonia" = "Other\nPneumonia")) +
  xlab("") +
  ylab("Gene Counts") +
  facet_wrap(~gene_name, scale = "free_y", ncol = 3) +
  geom_signif(inherit.aes = F, 
              data = gene_comps,
              aes(xmin = numerator, xmax = denominator, annotations = label, y_position = y_log10),
              tip_length = 0,
              manual=TRUE,
              family = "Arial",
              textsize = 4) +
theme_bw(base_family = "Arial", base_size = 32) +
theme(legend.position = "none",
         text = element_text(family = "Arial"),
         axis.text.x = element_blank(),
         axis.ticks.x = element_blank(),
         plot.title = element_text(hjust = 0.5),
         strip.background = element_blank())

 marker_gene_plot
```
   
```{r}
cov2_kmeans_noclustering = readRDS("/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200721_k_means_unclustered.rds")
coverage_plot = readRDS("/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200803_cov2_coverage_plot.rds")
figS3_heatmap = cov2_kmeans_noclustering$plot
figS3_heatmap_gg = as.ggplot(figS3_heatmap)

layout = "ABB\nABB\nABB\nABB\n#BB\nCBB\nDBB"
bulk_plot_fig3 = figS3_heatmap_gg +
  marker_gene_plot +
  sars_time_cor +
  coverage_plot +
    plot_layout(design = layout) +
    plot_annotation(tag_levels = 'a') & theme(plot.tag = element_text(size = 48))

bulk_plot_fig3
```
   
### Figure S3   
```{r}
md = as.data.frame(colData(mp_des))
col_types = unlist(lapply(md, class))
list_cols = names(col_types[col_types == "AsIs"])
safe_md = md %>% 
  dplyr::select(-c(all_of(list_cols))) %>% 
  as.data.frame()
write.csv(safe_md, "/projects/b1038/Pulmonary/rgrant/script_bulk/Analysis/200730_bulk_mp_metadata.csv")
```

